Tools & Platforms
We Have Proof AI Is Improving CX
Most of us want to see real benefits from AI, and for businesses, the contact center has long been one of the best use cases. The challenges are very real, and there is lots of incentive to find better solutions. With customer service now being framed as CX, the problem set becomes strategic for the business, so it’s bigger than just the contact center.
As such, the stakes are higher now, and technology investments are no longer about making incremental improvements. CX leaders – and business leaders – need to be ready to re-think everything they do in the name of serving customers. The more rooted the contact center is in legacy technology, the more transformational the change needs to be. With the right approach, the outcomes can meet this brief, especially in terms of elevating CX, and making customers feel more valued than ever before.
With all the hype, contact centers are being led to believe that AI is the right – and perhaps only – approach for them to follow. But, how can technology decision-makers know for sure? There are real results in the market, and to illustrate, I have some takeaways from a recent vendor event.
NiCE Interactions 2025 – Making it Real
All CX vendors aspire to deliver that right approach, but with AI evolving so fast, it’s difficult to tell who is getting real results. Any vendor can show tangible, AI-driven outcomes around standard KPIs such as time to answer or handle times, but these metrics are largely about automation.
While valuable, it’s not transformational, and what CX leaders should really be looking for are business-level outcomes that reflect a more strategic approach to AI. Automation is part of that, but AI needs to also address business drivers such as customer retention, marketing efficacy, agent empowerment, operational efficiency, compliance, data security, etc.
When thinking along those lines, the bar becomes higher for enterprises when partnering with CX vendors. They need a richer sense of what’s real, not just for the incremental benefits, but also the bigger picture where AI helps CX align with their business strategy.
That may be asking a lot, but I saw solid evidence of that at the recent NiCE Interactions 2025 event in Las Vegas. Aside from showcasing major strides with AI and their CXone platform from last year’s Interactions event, this was the first time most of us saw new-ish CEO Scott Russell. The company also just did a branding refresh to reflect their “NiCE world” product promise, which behind the scenes is largely powered by AI.
While agentic AI is all the rage right now, it’s just one of many touch points along the CX spectrum for AI. Before moving on to customer successes, it’s worth noting how extensively AI is embedded throughout NiCE’s CXone platform, as this is a big part of making that product promise real.
A few examples of these AI CX touchpoints include Topic AI, where unstructured transcripts are turned into structured data to enrich their LLMs; using Copilot to augment agent performance; Agent Builder to automate workflows; and Mpower Desk to make all tasks visible on one screen in real time, integrating front and back-office operations on a single platform.
Real Results from Real Customers
Impressive as all this is, the best proof points came directly from the customers themselves. Over the two days of sessions, we heard from six Tier-1 customers, each of whom explained how AI aligned with the company’s broader business priorities and initiatives while also driving better CX outcomes.
Tangible CX outcomes were cited, but just as important, we heard how these AI capabilities are helping them understand and meet the expectations of today’s customers – something all of them were struggling to do before. Here are two select examples, and note how they are from very different types of businesses; NiCE’s capabilities are not specific to a particular vertical, meaning that all CX leaders should be thinking along these lines for AI.
Arun Chandra, Disney
The first customer success story was from Arun Chandra, SVP of CX at Disney. I would argue that Disney sets the bar for how successfully brands tell stories, and the company’s narrative here was about customer journey being a form of storytelling. Chandra talked about the importance of upholding the brand in everything they do, and being the best at everything they do. As such, when it came to using AI for CX, Disney needed a partner with the best technology, the best AI safeguards, and the ability to do both at scale.
Knowing their AI deployment with NiCE would be safe, Disney has been able to deploy a mix of human and virtual agents for seamless CX to deliver a more modern form of customer service.
In terms of supporting the Disney brand, this new approach for using AI with CX also aligns well with Disney being a leading adopter of cinematic technology for movie making. While no AI metrics were shared here, the impact on a strategic level is real, and is a great example for how some customers are looking for more than tangible outcomes when choosing a CX partner for AI.
Brendan Mulryan, H&R Block
As VP of CX, Mulryan explained tax returns could benefit from modernizing their approach to customer service. With a nationwide network of retail locations to support millions of customers, a consumer-facing financial services company is an ideal use case for AI. Not only must call routing be intelligent enough to route inquiries to the nearest location, but the customer support must scale up for traffic peaks during tax season that are unlike most any other type of business.
Most of H&R Block’s inquiries are telephony-centric, simple tasks like setting appointments to come in to meet with a tax preparer. While not looking to reinvent the customer journey, H&R Block’s core need was to uplevel their IVA, especially during tax season. With NiCE Autopilot, the company was able to automate these inquiries, along with providing an SMS offramp in cases where customers preferred to use messaging instead of voice.
Not only does this improvement make the process of tax filing more efficient for everyone, but a better CX also strengthens customer loyalty. In terms of operational efficiency, Mulryan reported a 63% containment rate with NiCE. I don’t think he shared what that level was previously, but it clearly was an improvement, where almost two thirds of all calls could be fully automated.
That alone might be enough to justify deploying AI, but as with Disney – and other customer success stories – the strategic drivers were major considerations in choosing a technology partner. For H&R Block, this would be partnering with a vendor that could support their Next 2030 plan, and how improving CX is more than just serving the customer well for this year’s return.
The bigger goal is to “empower financial freedom for the client through trust and technology”, where the focus is on the lifetime value of each customer. Technology is key to building trust in any business, but especially here, when dealing with highly-sensitive personal financial data. Brendan cited a data point to support a high level of trust, showing that 78% intend to return after deploying with NiCE. That’s another good metric to show the real impact of AI on CX.
On a more strategic level, he talked about the need for AI to derive new insights from customer interactions to allow both agents and tax preparers to provide more personalized forms of service. This drives new value, not just for identifying new areas to provide additional services during tax time, but throughout the entire year. As such, similar to Disney, H&R Block had specific objectives, as well as transformational aspirations for CX, making this more than just an exercise in modernizing self-service.
The Takeaway for Enterprise Technology Leaders
Across these customer success stories – along with others during the breakout sessions – NiCE is clearly delivering real results with AI. As with any showcase event for customers, prospects and partners, there was also AI hype. Yes, the hype is real, but so are the results, and for the time being, the two go hand-in-hand with vendor messaging.
At Interactions, attendees did get a taste of some real benchmarks with AI, so it would be a mistake for CX leaders to hold off on AI until there’s more proof, and/or for the hype to die down. To that, I would cite Bryan Mulryan’s parting message about how the “cost of inaction” is high, especially with AI changing so quickly.
Along with that, he noted the need to rethink notions of ROI with these new technologies. The “right approach” for CX leaders is about achieving transformational outcomes with AI, and not just looking for operational efficiencies. Performance metrics do validate AI as being real, but so do the transformational outcomes that go beyond the numbers. That was a common theme across all the customer success stories, and when considering partners for AI and CX, the reality check needs to be a mix of both.
Tools & Platforms
Employers struggle to identify real candidates
India’s job sector is undergoing a major transformation, with excessive dependencies on Artificial Intelligence by freshers becoming a complex challenge for recruiters in the country. The AI era has become a double-edged sword for companies–while productivity has improved, over-reliance on AI technology has impacted employees’ critical thinking, originality, and problem-solving traits.
Last month, US-based Massachusetts Institute of Technology (MIT) revealed shocking details about people who use OpenAI’s ChatGPT tool significantly in their routine. The study concluded that ChatGPT users have lower brain engagement and consistently “underperformed” at the neural, linguistic, and behavioural level. Notably, Mary Meeker’s research on AI usage trends discovered that India tops the chart with the highest ChatGPT mobile app users globally, at 14 percent.
Mita Brahma, HR Head at NIIT, said that employees’ over-dependency on AI is a massive threat for recruiters that is looming in the job sector currently. “Employees’ foundational cognitive and collaborative skills are not developed due to AI dependencies,” she added, “This can lead to tech-dependent superficial capabilities that don’t translate into real-world performance”.
Arindam Mukherjee, co-founder of the skilling platform NextLeap, said he has observed a surge in fake resumes that are ATS-compliant and do not give a true picture of the candidate’s real skills.
“AI agents can now apply for jobs on your behalf. AI resume builders can make your resume look like you are the best candidate, AI tools can complete the take-home assignment in minutes, and AI interview co-pilots can run in the background, assisting you in your virtual interview”.
Anil Ethanur, Co-founder, Xpheno – a specialist staffing firm, underscored that enterprises are not just facing a challenge of ‘wrong hires’, but also ‘wrong drops’ in the AI-era. Ethanur said that there are a lot of ‘false positives’ candidates in the AI ecosystem, who are disguised as ‘ideal fit’ employees. “The noise of and from AI-enhanced resumes is a significant dilution of the quality of recruitment processes and also causes cost-time-&-resource wastage for employers,” according to Ethanur. Besides, AI tools have also been noted to cause ‘false negatives’ where candidates with a good fit get wrongly knocked out as low fits. “The chances enterprises incurring higher costs of ‘wrong hires’ are much higher in the current stage of the AI era,” he added.
Pranay Kale, Chief Revenue & Growth Officer, foundit, said that AI tools like ChatGPT, GitHub Copilot, and AI-enhanced resume builders have become second nature to younger job seekers. Therefore, Kale said that, “The Line between AI-assisted performance and actual capability is becoming increasingly blurred”.
While AI has crossed industries and functions, experts told Storyboard18 that sectors where creativity and judgment are central should be cautious when they onboard a new employee, particularly with 0-5 years of experience, into their organization. For instance, fields where content creation is a key task – research and development, publishing, media, advertisement, and journalism- should select the candidates carefully, Brahma said.
“In these fields, an overdependence on generative AI tools like ChatGPT without domain depth can lead to poor judgment, flawed insights, or even compliance risks. Hence, hiring in these sectors must include rigorous domain-specific assessments, ethical reasoning tests, and real-world simulations,” she said.
According to TeamLease Shantanu Rooj, industries that rely heavily on analytical thinking, ethical reasoning, and real-time problem-solving must be more deliberate and rigorous during hiring. Sectors such as consulting, financial services, legal advisory, and research demand professionals who can interpret nuance, deal with ambiguity, and make judgment calls based on context – all areas where AI currently falls short. Rooj added that education sector can also take a hit if the recruitment of teachers is not done correctly. “Teachers and professors who are overly dependent on AI tools risk diluting the learning experience rather than enriching it”.
Experts unanimously agreed that the hiring process should measure independent cognition, contextual reasoning, and original problem-solving skills that AI alone cannot supply when hiring a professional.
Dr Sangeeta Chhabra, Co-Founder & Executive Director, AceCloud, added, “leaders must go beyond assessing technical expertise and focus on attributes such as problem solving, adaptability, and the ability to collaborate effectively with intelligent systems to filter the right talent”.
Ankit Aggarwal, founder & CEO of Unstop, suggested that founders look beyond the resumes and give students real-time problems from solving different brands to help them showcase their ideas and problem-solving abilities.
Aggarwal said that “hackathons, coding challenges, case study competitions, quizzes,” can help in testing the real skills of the employees.
‘Dangers of over-reliance on AI’
According to Kale, the automation bias could contribute to structural unemployment and skill atrophy in certain sectors. Kale says that AI may erode critical thinking, problem-solving, and creativity, especially among early-career professionals. “If individuals lean too heavily on AI to automate outputs or make decisions without understanding the ‘why’ behind them, we risk developing a workforce that is skilled in using tools but lacks foundational cognitive depth,” Kale argued.
In contrast, Ethanur said that AI addiction will not lead to higher unemployment rates. He projected that a significant change in the job market will be driven by the mainstream arrival of AI in low to mid-cognitive functions. “The phase when this redefinition happens on a large scale will have to coincide with the arrival of sufficient AI-enabled and AI-dependent talent pools into mainstream employment”.
Rooj upheld that the next decade will not be defined by AI replacing people but by people who can meaningfully work with AI. For instance, roles like “prompt engineering, AI oversight, ethical data governance, and human-AI interface management” will gain traction.
“AI should empower, not diminish, the human edge, and it’s up to all of us to ensure we strike that balance,” Chhabra noted.
Tools & Platforms
Chinese AI stocks to extend DeepSeek-driven run as Beijing counts on growth boost
“AI will probably become a key driver for China’s modernisation,” said Yao Pei, an analyst at Huachuang Securities, in a report this month. “There are lots of catalysts for AI, and AI is expected to penetrate into every industry,” notably electronics, computing and media, Yao said.
Unlike the US, which had an edge in AI computing, China was focused on efficiency – emphasising revenue generated by AI-enabled offerings and cost savings achieved through high productivity, the US investment bank said.
Tools & Platforms
AI infrastructure startup LangChain reportedly raises $100M at $1.1B valuation
Artificial intelligence infrastructure, developer tools, observability and workflow orchestration company LangChain Inc. has reportedly raised $100 million in new funding on a $1.1 billion valuation.
The news that the company was raising a new round was first reported today by TechCrunch, with Forbes later claiming that the round had already been raised and closed. LangChain has not confirmed the details.
Founded in 2022, LangChain builds infrastructure and tools that are designed to make it easier for developers and companies to create applications powered by large language models. The company offers modular components to connect LLMs with data, tools, application programming interfaces and workflows, allowing for more advanced and interactive AI behavior.
LangChain’s core offering is a framework that lets developers chain together calls to LLMs, search systems and other tools. The approach supports complex multi-step reasoning, agent-based workflows and Retrieval-Augmented Generation pipelines.
The company has various tools that assist users in managing and dealing with LLMs, including LangSmith, a managed platform for debugging, testing and monitoring LLM applications. LangSmith helps teams identify where LLM behavior goes wrong, track usage and improve performance with observability built specifically for AI applications.
There’s also LangServe, a tool for turning LangChain applications into production-ready APIs. The tool makes it easier to deploy and scale language model workflows in real environments without building infrastructure from scratch.
The company’s tools integrate with a wide range of third-party tools, including vector databases, cloud platforms, API connectors and prompt management systems, making them suitable for everything from chatbots and copilots to complex enterprise workflows.
Coming into its new funding round, LangChain had previously raised $35 million over two rounds, according to data from Tracxn. Investors in the company include Benchmark Capital Management Company, Sequoia Capital Operations and Amplify Partners.
Harrison Chase, founder and chief executive officer of LangChain, spoke with theCUBE, SiliconANGLE Media’s livestreaming studio, in April, when he discussed how LangChain supports AI-based app development and exploration:
Photo: LangChain
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