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Its hard to believe, but were only a few weeks away from Halloween, and after that comes Novemberand the unofficial start of the holiday season. If you travel during this time, youll know that finding cheap flights can be difficult. To get the best prices, people traditionally turn to comparison sites like Kayak and SkyScanner. However, as artificial intelligence seems to be taking over everything, and the tech industry wont stop shouting about its benefits, I decided to try three conversational AI tools to see if they could help me find the cheapest flight deal for the Thanksgiving period. Heres how that went. Flights listed on ChatGPT The first tool I turned to was AI chatbot king, ChatGPT. I gave it the following prompt for a hypothetical holiday trip, which is the exact same prompt I issued the other AI chatbots I tried for this article: I want you to find me the cheapest tickets for a round-trip flight for this Thanksgiving period. My departure city is New York City, and my destination city is Dallas. ChatGPT asked me a series of questions about trip specifics, including exact dates and preferred airports, or gave me the option for defaults it selected (all NYC DFW/DAL airports from Wednesday, November 26, 2025 to Sunday, November 30, 2025). I chose the latter. OpenAIs chatbot then spit out a bunch of information, including which airlines and airports had the cheapest options, and asked me if I wanted to see further results on specific itineraries. It even offered to show me total landed cost options (which means it would let me know how much the flight would cost if I checked bags, too). It also offered to set up price alerts for me. But it didnt stop there. As ChatGPT will carry on a conversation for as long as you want (and as I didnt want to ask and answer questions for 30 minutes), the chatbot also gave me three ABC options: A Show me the three absolute cheapest round-trip itineraries (all carriers, show baggage fees). B Show me the three best nonstop options (if any) ranked by total cost & convenience. C Compare a cheap Spirit/Frontier itinerary versus a reliable nonstop (AA/Delta) including checked-bag costs. [ChatGPT previously told me low cost carriers often show the lowest base fares]. I chose option A. Ultimately, ChatGPT returned three itinerary options with the absolute cheapest with one checked bag being between $190 and $220. It also gave me a direct link to the carriers website so I could book that option. Flights listed on Google Flight Deals Now that I had ChatGPTs answer, I next gave the same prompt to Googles new Flight Deals, its AI-powered Google Flights search tool. Google launched Flight Deals last month, billing it as an AI-powered search tool within Google Flights that is designed for flexible travelers whose number one goal is saving money on their next trip. Flight Deals lets you prompt the service like you would as though youre talking to a friendin natural languageand it will return flight itineraries that best fit your needs. I entered the same prompt I used with ChatGPT. Frustratingly, Flight Deals then asked me to confirm where I was flying from. I replied with NYC and then had to also select New York from the drop-down menu. However, I only received one result: a $249 nonstop United flight from Monday, November 24, to Friday, November 28. Flight Deals said it checked departures between Nov 24 and Nov 27, and returns between November 28 and December 1, which generally matches the Thanksgiving period I indicated in my prompt. A disclaimer for the results stated that The results shown are flights that are either significantly cheaper than usual for a route, time of year, trip length, and seating class, or are among the lowest-priced options for destinations that match your search. Unlike ChatGPT, Google Flight Deals did not allow me to ask follow-up questions or provide any tips on finding cheap flights. It also didnt tell me whether the $249 flight included checked baggage. Clicking on the sole result took me to Google Flights traditional interface, which showed additional flight results. Flights listed on iMean.ai Finally, I gave the same prompt I used for the others to iMean.ai, one of a growing number of dedicated conversational AI travel assistants. Even though iMean.ai’s interface looks like a more colorful version of ChatGPT, the sites AI agent didnt waste time asking me questions in an attempt to refine my prompt like OpenAIs chatbot did. Instead, it informed me that it searched through 302 flight options and determined that the best itinerary matching my query was one that split the departure and return flights between two airlines. The outbound flight leaves New York City on Wednesday, November 26, and the return flight leaves Dallas on Sunday, November 30. The total cost: $334. iMean.ai’s agent, like ChatGPT, provided me with the option to continue chatting with it to ask more questions or refine my needs. And unlike ChatGPT, iMean.ai displayed the results in a useful split-screen interface that helpfully laid out details, such as flight times, for each leg of the trip. Clicking on the accompanying View button took me to Kayak, where I could buy the selected tickets. Should you use AI chatbots to find cheap flights? In the end, consulting with three different AI tools resulted in the agents returning three different flight options for my hypothetical Thanksgiving trip, all on different dates, different airlines, and at different price points (ChatGPT: $190 to $220, Google Flight Deals: $249, iMean.ai: $334). Based on price alone, ChatGPT found me the cheapest tickets for a flight from New York City to Dallas during the Thanksgiving period. But though I was happy with the price, the experience of using AI chatbots to help me find cheap flight deals left me with a nagging feeling: uncertainty. ChatGPT presented me with too many questions and options. I felt that if I kept engaging with it, I would be sucked into a never-ending succession of possibilities that would make it nearly impossible to choose. When was the right time to stop prompting and make a choice? I didnt know. Conversely, Google Flight Deals provided me with a single option. This left me unsure as to whether it was actually the best choice (according to ChatGPT, no). iMean.ai was a mix of the two. It was conversational like ChatGPT, but provided fewer options, like Google Flights. However, it also presented me with an option that was more expensive than the ones both ChatGPT and Google Flight Deals presented, leaving me questioning its results. Ultimately, my experience with the three chatbots left me wanting to return to the traditional flight comparison websites, like Skyscanner and Kayak, that I am used to. If youre thinking of using chatbots to find deals on flightsfor this upcoming holiday season, it cant hurt to check out various AI agents to see what information they return, but Id still check the results of any AI recommendations against the results of traditional flight checking tools.
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E-Commerce
Nearly every company I work with is focused on using AI to drive productivity and efficiency. They are starting to see real gains, and thats leading to excitement about AIs future potential. However, AI used to drive efficiency is only the starting line, and theres real risk if we stop there. In my work with Fortune 500 leaders across the C-suite, from chief HR officers (CHROs) to CTOs and CMOs, Ive seen that the very best organizations recognize a bigger opportunity: using AI to help managers build connection and trust with their teams. The companies that are able to leverage AI both to drive efficiency gains and to build highly motivated teams will be the ones that come out ahead. If youre only using AI for productivity, youre at risk AI is transforming work, and nearly every company I talk with is applying it to boost productivity from automating tasks to streamlining workflows and scaling output like coding and design. However, if we think about AI only as a tool for efficiency and cost cutting, were missing the bigger picture. Worse, we risk widening the trust gap that already exists in so many workplaces. The data is striking. A recent Upwork survey of 2,500 global workers, including 1,250 C-suite executives, found that 67% of top AI performers said they trust AI more than their colleagues, and 64% said they have a better relationship with AI than with their teammates. There is real risk present in those statements. At the end of the day, even with increased AI use, humans still have to work together to get things done. If we dont trust each other, efficiency gains from AI may be lost as organizations get mired in conflict, gossip, and fearthe hallmarks of low-trust company cultures. So, the question for every executive becomes: How do we equip the next generation of managers to use AI in service of connection? Here are three powerful ways. 1. AI as Your Leadership Memory The best managers Ive seen dont just lead, they remember. They pick up on the way individuals prefer feedback, they create a spark by recognizing people in a unique way, and they remember those personal details that tell someone they’re not just a cog in the wheel. Those moments of recognition build trust and loyalty. But today’s leaders are stretched. Teams are bigger, hierarchies flatter, and we simply cant hold all of that in our heads. As a leader myself, I often struggle to remember every detail about how each person on my team prefers to work or communicate. And every manager Ive coached has felt the same, because its hard to stay personal when youre juggling so much. Thats where AI steps in to reinforce what you already do best. Imagine before a one-on-one, your AI leadership memory gently reminds you that your direct report prefers written feedback over verbal, or that last time you spoke, they mentioned their childs soccer tournament. It can nudge you with thoughtful opening lines, and maybe even help you frame a difficult message so it lands in the best possible way. Thats not replacing the personal touch, its enabling it to scale. Leaders can use tools like Rising Team that automatically pull in insights that colleagues have shared, or manually upload materialslike personality assessmentsthat their team members have chosen to share. That way the AI can surface those details when they are helpful, without needing to use any private information. AI is helping you remember what matters, in the moment that it matters. 2. AI as Your Coaching Partner Some of the most meaningful moments in peoples careers come from the hardest conversations. Great leaders can deliver constructive feedback that helps people grow, diffuse conflict in a way that builds trust, and help teams be resilient amidst major challenges. Many managers just freeze, or wing it in conversations like these, not because they dont care, but because they dont know how to approach them and dont have a way to practice. And today, HR business partners can’t be there for every one of those moments. What if you could practice, and have your teams real dynamics baked in? Ive seen this dramatically shift things. Role-play with AI tailored to your specific engineer who needs time to process, or your marketer who craves blunt feedbackthats when AI coaching becomes real, actionable readiness. By practicing with a tool that knows your people, managers show up with clarity, empathy, and trust. 3. AI as Your Team Experience Builder Connection doesnt just happen in one-on-ones. Teams build trust and alignment in shared experiences, whether its learning a critical skill, tackling a big strategy shift, or building insights about how to work together as a team. But creating and facilitating those sessions takes time and expertise, and most managers dont have the time or the support to do it well. This is where AI can help. Think of it like working with a facilitation proone who knows theory and your team context. It can layer in warm-ups, activity ideas, reflective questions, and even capture what people say, track whos engaged, and surface next steps. With help from AI, managers are now capable of bringing their teams together to build trust and connection in synchronous experiences that were too hard or expensive to do before. As an executive, imagine rolling out a new company initiative or framework. Instead of relying on slides and top-down presentations, AI can now help your managers lead team sessions with reflective prompts, collaborative exercises, and clear action plans. This ensures that experiences across the organization are consistent, measurable, and engaging. Beyond Productivity: Building a System for Connected Leadership AI is often portrayed as a catalyst for productivity. And yes, its great for that, but I believe the real frontier is AI as a force to drive connectiona leadership system for modern teams. Because the future isn’t about choosing AI or humanity. Its about how we use AI to amplify our humanity, and build teams that are not just productive, but also high trust, resilient, and great at delivering results together.
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E-Commerce
On Friday, shares of U.S. rare earth stocks rose after President Donald Trump accused China of strict export controls and threatened a massive increase of tariffs on Chinese products” once againhowever, the comments ended up triggering a market sell-off. At issue: China imposed stricter export controls on critical rare earth minerals that the U.S. technology industry depends on for electronics, robotics, and electric vehicles, which are also critical for the defense industry, per CNBC. The news network also reported China will now require foreign entities to obtain a license to export products that contain rare earths worth 0.1% or more of the goods value. I will be forced, as President of the United States of America, to financially counter their move, Trump said on Truth Social, his social platform. There are many other countermeasures that are, likewise, under serious consideration. Stock market effect After Trump announced his plans to raise Beijing’s tariffs, a number of rare earth stocks went up: USA Rare Earth surged 19%, Energy Fuels rose 10%, and MP Materials gained 15%, CNBC reported. By the end of the day, those stocks had lost some of those gains, with USA Rare Earth up only 6%, Energy Fuels up just 4%, while MP Materials maintained its position, up 10%. On the other hand, the global cryptocurrency market lost nearly $125 billion within hours. The total global crypto market capitalization was at $4.05 trillion, down 3.94% in the last 24 hours, marking a 83.68% change from one year ago. Bitcoin’s (BTC) market cap was at $2.32 trillion, representing a Bitcoin dominance of 57.23%. Meanwhile, stablecoins’ market cap was at $310 Billion, with a 7.66% share of the total crypto market cap, according to CoinGecko. The sell-off spread to the stock market as well, where the S&P 500 lost 2.71%, the Dow Jones Industrial Average closed the day down 878.82 points (1.9%), and the Nasdaq Composite fell 3.56%, CNBC reported. That sell-off included Nvidia (NVDA), which was down almost 5% at the close of the market.
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E-Commerce
The cracks in postmodern economic theories are visible. Theyve spilled into politics, with governments slashing budgets worldwide. The spark came from Richard Thaler (Nudge) and Daniel Kahneman (Thinking, Fast and Slow), but the roots run deeper. In 1978, Herbert Simon won the first Nobel Prize for behavioral economics. Thaler later brought the field into public view with his anomalies articles in the Journal of Economic Perspectives between 1987 and 1990. The message was clear: People act based on their environments. Psychology had already demonstrated this in clinical practice; economics eventually followed. With that, homo economicusthe hyperrational actor of industrial modernitydied. Along with him went the playbook of meritocracy, technical determinism, and cold rationality. In his place rose concepts like culture, institutions, purpose, inclusive HR, gender equality, quotas, and languagesocial dynamics grounded in behavioral insights. As service economies expanded, requiring soft skills more than industrial hard skills, behavioral economics spread. But the field made a major oversight: It never invited accounting to the conversation. THE ACCOUNTING BLIND SPOT Accounting frameworks from FAF and IFRS are still designed for industrial modernity: Only positive, immediate cash flows count as value. Everything else is classified as a cost. That means the way a company treats suppliers, employees, communities, and the environment is booked as a loss, disconnected from value creation. Even ESG initiatives are paradoxically punished by the very systems that claim to encourage them. Consider a practical case: a company with 10,000 Google reviews averaging 4.6 stars. From a statistical perspective, this dataset holds weight. It is large enough to fall under the law of large numbersvalid, representative, and statistically significant. It is a voluntary response sample with real-world significance, combining quantitative and qualitative depth. Most importantly, it suggests correlation with causation: Employees, suppliers, and communities are treated with respect and professionalism. That number is not just a reputation score. Its a direct indicator of ESG performance and long-term value creation. It also signals that leadership is competent and that the company is likely to sustain future cash flows, impacting valuation itself. Yet none of this is captured on the balance sheet. FROM BEHAVIORISM TO HYPER-MODERNISM We are entering what could be called hypermodernism, a necessary blend of behavioral insights and rationalist rigor. But the dialogue has barely started. Take HR practices, or todays people analytics. Some companies still measure screen time as a proxy for productivity. Few integrate stakeholder feedback on employee well-being, family quality of life, or the actual value of deliverables. Meanwhile, technology has already solved problems of scale. Large language models like ChatGPT process data in ways far more complex than corporate metrics. A simple 10-word sentence is represented by around 257,000 parameters, calculated in hundredths of a second. Training involves millions of such sentences, across billions of parameters. If AI systems can process that complexity, organizations can certainly design models with 100-200 parameters to identify talent, monitor well-being, and measure real performance. They can even share these benchmarks across industries, just as the scientific community shares open datasets. With web scraping, API mining, sentiment analysis, metadata extraction, and time-series tracking, organizations can measure behaviors and relationships with a precision unavailable to earlier generations. MEASURE WHAT TRULY CREATES VALUE This is the opportunity: to move beyond the hard-line modernist models built to exclude unexplainable asymmetries from the balance sheet, and instead bring those very asymmetries into view through multiparameter models. If we genuinely want to assign value to diversity, inclusion, and the social dynamics that generate wealth, we must measure these effects, not dismiss them as expenses. That requires accounting to catch up, and for Nobel-winning thinkers to help rewrite the rules. FURTHER READINGS This debate isnt isolated. Harvards Impact-Weighted Accounts Project is working to embed social and environmental externalities directly into financial statements, while frameworks like Context-Based Sustainability argue that performance should be judged against ecological and social thresholds. At the same time, critiques of ESG ratings reveal how fragmented and inconsistent todays measures are. New approachesranging from relational metrics of trust and community well-being to AI-driven sentiment analysisare emerging. All point to the same conclusion: Accounting must evolve to treat culture, relationships, and impact not as costs, but as core drivers of long-term value creation. Rodrigo Magnago is researcher at RMagnago Critical Thinking.
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E-Commerce
Customer experience is entering the sci-fi age: knowing and understanding customers on an individual level, providing personalized service, and dedicated moments. All of this is becoming possible thanks to technological innovation. And as it shifts, were moving beyond the age of reactive service, where customer satisfaction was measured by stale, bi-annual surveys. Were entering an era of proactive, predictive customer care. Companies missions today should be to transform every interaction into a moment of loyalty and growth, a goal we are working to achieve through our latest in-house innovation: the Customer Experience Index, or CXI. While many companies talk about focusing on customer experience, we wanted to move beyond buzzwords and create a framework that allows for proactive, real-time management of our customer relationships. Traditional methods of measuring customer satisfaction, like the bi-annual surveys mentioned earlier, often fall short. They only provide a snapshot in time, leaving significant gaps in our understanding of daily customer sentiment and needs. To truly drive change and speak to our customers on their current standing, we needed something morea tool that could provide real-time, actionable insights at a massive scale. UNDERSTAND THE EXPERIENCE WITHOUT ASKING THE QUESTION This is where the Customer Experience Index comes in. It is an AI-powered, real-time measurement framework that transforms raw customer sentiment into predictive, actionable insights, which in turn works to enhance loyalty, reduce churn, and drive business growth. Its not just a metric; its a predictive engine built on over 200 AI/machine learning models and generative AI insights. The CXI evaluates a wide range of factors, including customer behavior, network performance, service interactions, and even external market signals like mergers and acquisitions. With a 98% accuracy rate compared to traditional NPS surveys, CXI gives us near instant, account-level insights that allow us to anticipate and address issues before they escalate for our business customers. The ability of CXI to understand the experience without asking the question, is a game changer. It enables us to shift from a reactive to a proactive customer experience model. For example, our service teams no longer just react to inbound service calls. Instead, an AI-powered tool called “Right to Sell,” fed by CXI data, provides real-time guidance to agents during a service call. A green light might signal that a customer is satisfied and an agent can suggest a new product or service, while a red light indicates the customer is frustrated, and the agent should focus on resolving their issue and building brand loyalty. This allows our service team to transition from a cost center to a value-driving part of the business. This shift also frees up our dedicated sales teams to focus on more strategic selling opportunities. This approachs impact is already clear. In the first half of 2025 alone, around 20% of our accounts saw an improvement in their CXI scores. These customers are more likely to stay with us, buy more from us, and even become evangelists of our services. These results demonstrate how we are translating customer insights into tangible business outcomes. PERSONALIZATION MATTERS Beyond the numbers, the CXI represents a deeper, more empathetic understanding of our customers’ journeys. By integrating conversational intelligence that analyzes interactions across all communication channels (with the customer opting in)including phone calls, chat, and emailwe can score customer sentiment and track our commitments in real time. This allows our specialized teams to proactively engage with customers, sometimes even before the customer is aware of the problem, and provide a higher level of care. Ultimately, our ability to deliver winning customer experiences is tied to this level of personalization. By understanding our customers both rationally and emotionally, we can anticipate their needs, offer proactive solutions, and provide a seamless, intuitive experience across all touchpoints. Our work with CXI is not just about tracking satisfaction; it’s about actively shaping a future where every interaction strengthens customer loyalty and fuels growth, propelling us toward our customer goals and solidifying our position as a leader in customer experience. Iris Meijer is chief product and marketing officer of Verizon Business.
Category:
E-Commerce
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