Back in 2023, Chief Executive Sebastian Siemiatkowski suggested AI could replace large parts of the company’s customer-service workforce. The remarks sparked pushback from employees and skepticism from customers, many of whom doubted whether the technology was advanced enough to provide empathy and reliability at scale.

Pivoting and Learning

Klarna’s first wave of AI adoption proved too rigid, with customers finding the experience inconsistent. The company now pivoted toward a blended approach: AI for speed and scale, humans for empathy and trust. That adjustment echoes a lesson resonating across industries. AI works best when it augments, rather than replaces, human agents.

The company’s focus on human-powered customer support shows how the firm is hiring again to ensure customers always have the option of speaking to a person. “From a brand perspective, a company perspective, I just think it’s so critical that you are clear to your customer that there will be always a human if you want,” Siemiatkowski told Bloomberg News, as reported by PYMNTS.

As Vinod Muthukrishnan, vice president and chief operating officer of Webex Customer Experience Solutions at Cisco, explained, many financial institutions are moving past pilots and into deployment.

“These firms are increasingly leveraging their AI focus on hyper-personalized CX [customer experience] such as personal financial advice or dynamic credit limit adjustments and offers, all enabled via real-time analytics,” he told PYMNTS. Retailers and service providers face similar opportunities, provided they align strategy with measurable ROI.

Five Areas for AI, Customer Care

1. Proactive Issue Resolution

AI can anticipate problems before customers complain. Declined payments, unexpected fees or delivery delays can be flagged and addressed in real time, turning frustration into loyalty. Most firms still operate reactively, in part because data remains siloed across payments, logistics and support and closing these gaps could sharply reduce call volumes.

2. Hyper-Personalized Support

Consumers now expect service that reflects their history and preferences. AI can tailor repayment options, loyalty incentives, or offers based on real-time data. Walmart, for example, has deployed AI-powered personalization tools to refine its app and eCommerce experience. Predictive analytics can also flag anomalies that suggest fraud or disputes, thereby reducing chargebacks. Yet many retailers still rely on generic scripts.

3. Multilingual, 24/7 Coverage

Global commerce does not keep office hours. AI chatbots and voice systems provide round-the-clock, multilingual support. New multimodal systems can handle voice, text, and even images, creating richer customer interactions. PYMNTS has reported that customers value this always-on flexibility, but many firms still lean on nine-to-five call centers or outsourced night shifts.

4. Sentiment Detection and Emotional Intelligence

Speed matters, but empathy builds loyalty. AI can read tone and phrasing in real time, alerting human agents when a customer is upset. This hybrid model ensures efficiency without sacrificing trust. Rezolve’s Brain Suite applies empathy-driven AI to reduce cart abandonment, which accounts for nearly 70% of lost online sales. Yet sentiment detection remains rare in many call centers.

5. Insights Beyond the Call Center

Complaints can expose flaws in checkout flows, packaging or design. AI can analyze these patterns, turning customer service into a source of business intelligence. Google’s Vision Match tools, for example, feed insights from shopping behavior back into product strategy. Few enterprises close this loop.

ROI as the Deciding Factor

For executives, ROI is the real test. Projects that fail to deliver lower handle times, better satisfaction scores, or reduced churn rarely scale. “AI as with any new technology risks adoption and integration without a clear strategic alignment,” Muthukrishnan warned. “Too many pilots or implementations can lead to a fragmented focus.”

 “We’re already in market with our AI agent for autonomous and scripted self-service,” Todd Fisher, CEO and co-founder of CallTrackingMetrics, told PYMNTS.  

“In a recent survey, 72% of respondents rated Webex AI Agent as equal, if not better, than a human agent. And our customers have reported an 85% reduction in agent call escalations, a 22% reduction in average handle time, and a 39% increase in CSAT [customer satisfaction] scores.” 

Questions and answers:

• What is the primary feature of the LRASM C-3 missile compared to earlier variants? It has nearly double the range of previous versions, with a range of about 1,000 miles, compared to 200 to 300 miles for the C-1 and 580 miles for the C-2.
• How does artificial intelligence enhance the LRASM C-3’s capabilities? AI helps the missile with autonomous mission planning, target discrimination, and attack coordination, adjust flight paths based on real-time data, identify and track moving targets, and adapt to changing conditions like jamming and interference.
• What can launch the LRASM C-3 missile? U.S. Air Force B-1B bombers, Navy F/A-18E/F Super Hornets, and F-35 Lightning II jets, with possible future launches from Navy ships and attack submarines.

PATUXENT RIVER NAS, Md. – U.S. Navy surface warfare experts are asking Lockheed Martin Corp. to move forward with developing the new LRASM C-3 anti-ship missile with double the range of previous versions.

Officials of the Naval Air Systems Command at Patuxent River Naval Air Station, Md., announced a $48.1 million order last month to the Lockheed Martin Missiles and Fire Control segment in Orlando, Fla., for engineering to establish the Long Range Anti-Ship Missile (LRASM) C-3 variant.

The subsonic LRASM is for attacking high-priority enemy surface warships like aircraft carriers, troop transport ships, and guided-missile cruisers from Navy, U.S. Air Force, and allied aircraft.

LRASM is designed to detect and destroy high-priority targets within groups of ships from extended ranges in electronic warfare jamming environments. It is a precision-guided, standoff anti-ship missile based on the Lockheed Martin Joint Air-to-Surface Standoff Missile-Extended Range (JASSM-ER).

1,000-mile range

The LRASM C-3 variant has a range of nearly 1,000 miles, compared to the 200-to-300-mile C-1 variant, and 580-mile range of the LRASM C-2 variant.

LRASM C-3 also introduces machine learning and advanced artificial intelligence (AI) algorithms to enhance autonomous mission planning, target discrimination, and attack coordination, even amid intense electronic warfare (EW) jamming.

The C-3 also can exchange information from military satellites, and has an enhanced imaging infrared and RF seeker for survivability and target identification.

The C-3 also can be launched form the Air Force from B-1B strategic jet bomber, as well as the Navy F/A-18E/F Super Hornet jet fighter-bomber and the F-35 Lightning II attack jet. Navy leaders also envision using the Navy MK 41 shipboard vertical launch system with the LRASM C-3, and are considering options to launch the missile from attack submarines.

Tell me more about applying artificial intelligence to missile guidance …

• Applying artificial intelligence to missile guidance will enhance precision, adapt to dynamic environments, and improve decision-making in real-time. AI can help missiles navigate autonomously by using real-time data from radar, infrared sensors, and GPS to adjust flight paths. AI also can help missiles visually identify targets from images or video feeds, and not only enhance the missile’s ability to recognize and track moving targets, but also to predict and follow moving targets even if they change direction or speed. Using AI, missile guidance systems can make real-time adjustments to their trajectory based on changing conditions like wind, RF interference, and jamming. Missiles also may use AI to other weapons in swarm tactics, and operate effectively against countermeasures.

Helping to extend the LRASM C-3’s range are an advanced multi-mode sensor suite; enhanced data exchange and communications; digital anti-jam GPS and navigation; and AI and machine learning capabilities.

The missile’s multi-mode sensor suite is expected to blend imaging infrared and RF sensors to help the weapon identify and attack targets. Its communications will have data links for secure real-time communication with satellites, drones, and strike aircraft.

Digital anti-jam GPS and navigation will provide midcourse guidance to target areas far beyond the effective range of traditional systems. AI and machine learning, meanwhile, should help the missile identify targets and plan its routes autonomously. The LRASM C-3 version should enter service next year.

On this order, Lockheed Martin will do the work in Orlando and Ocala, Fla.; and in Troy, Ala., and should be finished in November 2026. For more information contact Lockheed Martin Missiles and Fire Control online at https://www.lockheedmartin.com/en-us/products/long-range-anti-ship-missile.html, or Naval Air Systems Command at www.navair.navy.mil.

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