One of the four pillars of good conversation is levity. You needn’t be a comedian, you can but have some funTetra Images, LLC/Alamy Conversation lies at the heart of our relationships – yet many of us find it surprisingly hard to talk to others. We may feel anxious at the thought of making small talk with strangers and struggle to connect with the people who are closest to us. If that sounds familiar, Alison Wood Brooks hopes to help. She is a professor at Harvard Business School, where she teaches an oversubscribed course called “TALK: How to talk gooder in business and life”, and the author of a new book, Talk: The science of conversation and the art of being ourselves. Both offer four key principles for more meaningful exchanges. Conversations are inherently unpredictable, says Wood Brooks, but they follow certain rules – and knowing their architecture makes us more comfortable with what is outside of our control. New Scientist asked her about the best ways to apply this research to our own chats. David Robson: Talking about talking feels quite meta. Do you ever find yourself critiquing your own performance? Alison Wood Brooks: There are so many levels of “meta-ness”. I have often felt like I’m floating over the room, watching conversations unfold, even as I’m involved in them myself. I teach a course at Harvard, and [my students] all get to experience this feeling as well. There can be an uncomfortable period of hypervigilance, but I hope that dissipates over time as they develop better habits. There is a famous quote from Charlie Parker, who was a jazz saxophonist. He said something like, “Practise, practise, practise, and then when you get on stage, let it all go and just wail.” I think that’s my approach to conversation. Even when you’re hyper-aware of conversation dynamics, you have to remember the true delight of being with another human mind, and never lose the magic of being together. Think ahead, but once you’re talking, let it all go and just wail. Reading your book, I learned that a good way to enliven a conversation is to ask someone why they are passionate about what they do. So, where does your passion for conversation come from? I have two answers to this question. One is professional. Early in my professorship at Harvard, I had been studying emotions by exploring how people talk about their feelings and the balance between what we feel inside and how we express that to others. And I realised I just had this deep, profound interest in figuring out how people talk to each other about everything, not just their feelings. We now have scientific tools that allow us to capture conversations and analyse them at large scale. Natural language processing, machine learning, the advent of AI – all this allows us to take huge swathes of transcript data and process it much more efficiently. Receive a weekly dose of discovery in your inbox. Sign up to newsletter The personal answer is that I’m an identical twin, and I spent my whole life, from the moment I opened my newborn eyes, existing next to a person who’s an exact copy of myself. It was like observing myself at very close range, interacting with the world, interacting with other people. I could see when she said and did things well, and I could try to do that myself. And I saw when her jokes failed, or she stumbled over her words – I tried to avoid those mistakes. It was a very fortunate form of feedback that not a lot of people get. And then, as a twin, you’ve got this person sharing a bedroom, sharing all your clothes, going to all the same parties and playing on the same sports teams, so we were just constantly in conversation with each other. You reached this level of shared reality that is so incredible, and I’ve spent the rest of my life trying to help other people get there in their relationships, too. “TALK” cleverly captures your framework for better conversations: topics, asking, levity and kindness. Let’s start at the beginning. How should we decide what to talk about? My first piece of advice is to prepare. Some people do this naturally. They already think about the things that they should talk about with somebody before they see them. They should lean into this habit. Some of my students, however, think it’s crazy. They think preparation will make the conversation seem rigid and forced and overly scripted. But just because you’ve thought ahead about what you might talk about doesn’t mean you have to talk about those things once the conversation is underway. It does mean, however, that you always have an idea waiting for you when you’re not sure what to talk about next. Having just one topic in your back pocket can help you in those anxiety-ridden moments. It makes things more fluent, which is important for establishing a connection. Choosing a topic is not only important at the start of a conversation. We’re constantly making decisions about whether we should stay on one subject, drift to something else or totally shift gears and go somewhere wildly different. Sometimes the topic of conversation is obvious. Even then, knowing when to switch to a new one can be trickyMartin Parr/Magnum Photos What’s your advice when making these decisions? There are three very clear signs that suggest that it’s time to switch topics. The first is longer mutual pauses. The second is more uncomfortable laughter, which we use to fill the space that we would usually fill excitedly with good content. And the third sign is redundancy. Once you start repeating things that have already been said on the topic, it’s a sign that you should move to something else. After an average conversation, most people feel like they’ve covered the right number of topics. But if you ask people after conversations that didn’t go well, they’ll more often say that they didn’t talk about enough things, rather than that they talked about too many things. This suggests that a common mistake is lingering too long on a topic after you’ve squeezed all the juice out of it. The second element of TALK is asking questions. I think a lot of us have heard the advice to ask more questions, yet many people don’t apply it. Why do you think that is? Many years of research have shown that the human mind is remarkably egocentric. Often, we are so focused on our own perspective that we forget to even ask someone else to share what’s in their mind. Another reason is fear. You’re interested in the other person, and you know you should ask them questions, but you’re afraid of being too intrusive, or that you will reveal your own incompetence, because you feel you should know the answer already. What kinds of questions should we be asking – and avoiding? In the book, I talk about the power of follow-up questions that build on anything that your partner has just said. It shows that you heard them, that you care and that you want to know more. Even one follow-up question can springboard us away from shallow talk into something deeper and more meaningful. There are, however, some bad patterns of question asking, such as “boomerasking”. Michael Yeomans [at Imperial College London] and I have a recent paper about this, and oh my gosh, it’s been such fun to study. It’s a play on the word boomerang: it comes back to the person who threw it. If I ask you what you had for breakfast, and you tell me you had Special K and banana, and then I say, “Well, let me tell you about my breakfast, because, boy, was it delicious” – that’s boomerasking. Sometimes it’s a thinly veiled way of bragging or complaining, but sometimes I think people are genuinely interested to hear from their partner, but then the partner’s answer reminds them so much of their own life that they can’t help but start sharing their perspective. In our research, we have found that this makes your partner feel like you weren’t interested in their perspective, so it seems very insincere. Sharing your own perspective is important. It’s okay at some point to bring the conversation back to yourself. But don’t do it so soon that it makes your partner feel like you didn’t hear their answer or care about it. Research by Alison Wood Brooks includes a recent study on “boomerasking”, a pitfall you should avoid to make conversations flowJanelle Bruno What are the benefits of levity? When we think of conversations that haven’t gone well, we often think of moments of hostility, anger or disagreement, but a quiet killer of conversation is boredom. Levity is the antidote. These small moments of sparkle or fizz can pull us back in and make us feel engaged with each other again. Our research has shown that we give status and respect to people who make us feel good, so much so that in a group of people, a person who can land even one appropriate joke is more likely to be voted as the leader. And the joke doesn’t even need to be very funny! It’s the fact that they were confident enough to try it and competent enough to read the room. Do you have any practical steps that people can apply to generate levity, even if they’re not a natural comedian? Levity is not just about being funny. In fact, aiming to be a comedian is not the right goal. When we watch stand-up on Netflix, comedians have rehearsed those jokes and honed them and practised them for a long time, and they’re delivering them in a monologue to an audience. It’s a completely different task from a live conversation. In real dialogue, what everybody is looking for is to feel engaged, and that doesn’t require particularly funny jokes or elaborate stories. When you see opportunities to make it fun or lighten the mood, that’s what you need to grab. It can come through a change to a new, fresh topic, or calling back to things that you talked about earlier in the conversation or earlier in your relationship. These callbacks – which sometimes do refer to something funny – are such a nice way of showing that you’ve listened and remembered. A levity move could also involve giving sincere compliments to other people. When you think nice things, when you admire someone, make sure you say it out loud. This brings us to the last element of TALK: kindness. Why do we so often fail to be as kind as we would like? Wobbles in kindness often come back to our egocentrism. Research shows that we underestimate how much other people’s perspectives differ from our own, and we forget that we have the tools to ask other people directly in conversation for their perspective. Being a kinder conversationalist is about trying to focus on your partner’s perspective and then figuring what they need and helping them to get it. Finally, what is your number one tip for readers to have a better conversation the next time they speak to someone? Every conversation is surprisingly tricky and complex. When things don’t go perfectly, give yourself and others more grace. There will be trips and stumbles and then a little grace can go very, very far. Topics:

Ivy Grant, SVP of Strategy & Operations, Twilio June 13, 20255 Min Readpeshkova via alamy stockAsk technologists and enterprise leaders what they hope AI will deliver, and most will land on some iteration of the "T" word: transformation. No surprise, AI and its “cooler than you” cousin, generative AI (GenAI), have been hyped nonstop for the past 24 months. But therein lies the problem. Many organizations are rushing to implement AI without a grasp on the return on investment (ROI), leading to high spend and low impact. Without anchoring AI to clear friction points and acceleration opportunities, companies invite fatigue, anxiety and competitive risk. Two-thirds of C-suite execs say GenAI has created tension and division within their organizations; nearly half say it’s “tearing their company apart.” Most (71%) report adoption challenges; more than a third call it a massive disappointment. While AI's potential is irrefutable, companies need to reject the narrative of AI as a standalone strategy or transformational savior. Its true power is as a catalyst to amplify what already works and surface what could. Here are three principles to make that happen. 1. Start with friction, not function Many enterprises struggle with where to start when integrating AI. My advice: Start where the pain is greatest. Identify the processes that create the most friction and work backward from there. AI is a tool, not a solution. By mapping real pain points to AI use cases, you can hone investments to the ripest fruit rather than simply where it hangs at the lowest. Related:For example, one of our top sources of customer pain was troubleshooting undeliverable messages, which forced users to sift through error code documentation. To solve this, an AI assistant was introduced to detect anomalies, explain causes in natural language, and guide customers toward resolution. We achieved a 97% real-time resolution rate through a blend of conversational AI and live support. Most companies have long-standing friction points that support teams routinely explain. Or that you’ve developed organizational calluses over; problems considered “just the cost of doing business.” GenAI allows leaders to revisit these areas and reimagine what’s possible. 2. The need for (dual) speed We hear stories of leaders pushing an “all or nothing” version of AI transformation: Use AI to cut functional headcount or die. Rather than leading with a “stick” through wholesale transformation mandates or threats to budgets, we must recognize AI implementation as a fundamental culture change. Just as you wouldn't expect to transform your company culture overnight by edict, it's unreasonable to expect something different from your AI transformation. Related:Some leaders have a tendency to move faster than the innovation ability or comfort level of their people. Most functional leads aren’t obstinate in their slow adoption of AI tools, their long-held beliefs to run a process or to assess risks. We hired these leaders for their decades of experience in “what good looks like” and deep expertise in incremental improvements; then we expect them to suddenly define a futuristic vision that challenges their own beliefs. As executive leaders, we must give grace, space and plenty of “carrots” -- incentives, training, and support resources -- to help them reimagine complex workflows with AI. And, we must recognize that AI has the ability to make progress in ways that may not immediately create cost efficiencies, such as for operational improvements that require data cleansing, deep analytics, forecasting, dynamic pricing, and signal sensing. These aren’t the sexy parts of AI, but they’re the types of issues that require superhuman intelligence and complex problem-solving that AI was made for. 3. A flywheel of acceleration The other transformation that AI should support is creating faster and broader “test and learn” cycles. AI implementation is not a linear process with start here and end there. Organizations that want to leverage AI as a competitive advantage should establish use cases where AI can break down company silos and act as a catalyst to identify the next opportunity. That identifies the next as a flywheel of acceleration. This flywheel builds on accumulated learnings, making small successes into larger wins while avoiding costly AI disasters from rushed implementation. Related:For example, at Twilio we are building a customer intelligence platform that analyzes thousands of conversations to identify patterns and drive insights. If we see multiple customers mention a competitor's pricing, it could signal a take-out campaign. What once took weeks to recognize and escalate can now be done in near real-time and used for highly coordinated activations across marketing, product, sales, and other teams. With every AI acceleration win, we uncover more places to improve hand-offs, activation speed, and business decision-making. That flywheel of innovation is how true AI transformation begins to drive impactful business outcomes. Ideas to Fuel Your AI Strategy Organizations can accelerate their AI implementations through these simple shifts in approach: Revisit your long-standing friction points, both customer-facing and internal, across your organization -- particularly explore the ones you thought were “the cost of doing business” Don’t just look for where AI can reduce manual processes, but find the highly complex problems and start experimenting Support your functional experts with AI-driven training, resources, tools, and incentives to help them challenge their long-held beliefs about what works for the future Treat AI implementation as a cultural change that requires time, experimentation, learning, and carrots (not just sticks) Recognize that transformation starts with a flywheel of acceleration, where each new experiment can lead to the next big discovery The most impactful AI implementations don’t rush transformation; they strategically accelerate core capabilities and unlock new ones to drive measurable change. About the AuthorIvy GrantSVP of Strategy & Operations, Twilio Ivy Grant is Senior Vice President of Strategy & Operations at Twilio where she leads strategic planning, enterprise analytics, M&A Integration and is responsible for driving transformational initiatives that enable Twilio to continuously improve its operations. Prior to Twilio, Ivy’s career has balanced senior roles in strategy consulting at McKinsey & Company, Edelman and PwC with customer-centric operational roles at Walmart, Polo Ralph Lauren and tech startup Eversight Labs. She loves solo international travel, hugging exotic animals and boxing. Ivy has an MBA from NYU’s Stern School of Business and a BS in Applied Economics from Cornell University. See more from Ivy GrantReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like

Businesses in highly-regulated industries like financial services, insurance, pharmaceuticals, and health care are increasingly turning to AI-powered tools to streamline complex and sensitive tasks. Conversational AI-driven interfaces are helping hospitals to track the location and delivery of a patient’s time-sensitive cancer drugs. Generative AI chatbots are helping insurance customers answer questions and solve problems. And agentic AI systems are emerging to support financial services customers in making complex financial planning and budgeting decisions.  “Over the last 15 years of digital transformation, the orientation in many regulated sectors has been to look at digital technologies as a place to provide more cost-effective and meaningful customer experience and divert customers from higher-cost, more complex channels of service,” says Peter Neufeld, who leads the EY Studio+ digital and customer experience capability at EY for financial services companies in the UK, Europe, the Middle East, and Africa.  DOWNLOAD THE FULL REPORT For many, the “last mile” of the end-to-end customer journey can present a challenge. Services at this stage often involve much more complex interactions than the usual app or self-service portal can handle. This could be dealing with a challenging health diagnosis, addressing late mortgage payments, applying for government benefits, or understanding the lifestyle you can afford in retirement. “When we get into these more complex service needs, there’s a real bias toward human interaction,” says Neufeld. “We want to speak to someone, we want to understand whether we’re making a good decision, or we might want alternative views and perspectives.”  But these high-cost, high-touch interactions can be less than satisfying for customers when handled through a call center if, for example, technical systems are outdated or data sources are disconnected. Those kinds of problems ultimately lead to the possibility of complaints and lost business. Good customer experience is critical for the bottom line. Customers are 3.8 times more likely to make return purchases after a successful experience than after an unsuccessful one, according to Qualtrics. Intuitive AI-driven systems— supported by robust data infrastructure that can efficiently access and share information in real time— can boost the customer experience, even in complex or sensitive situations.  Download the full report. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.