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With new AI tools, integrations, and use cases coming out almost daily, I know it can be overwhelming to keep up with how AI is changing business. Take DeepSeek R1, for example. Created by Chinese company DeepSeek, the AI model turned the world on its head when it was first launched in January 2025. It disrupted the entire AI industry, helping researchers and marketers to do research, brainstorm, and generate content very quickly and at no cost. Most professionals believe AI innovations will be comparable to the industrial revolution. So, if you’re wondering exactly how AI is changing business, then you’ve come to the right place. At its core, I think the advent of AI means one big thing for the future of business: marketing, sales, and service professionals now have more time to work on complex, higher-impact work while AI takes care of mundane tasks. Here are a few ways AI’s already changing the workplace and what we can look forward to, according to our State of AI survey of 1,350 U.S. professionals. Table of Contents How AI Is Used in Business Today How AI Will Impact Business in the Future The Potential Dangers of AI in Business How AI Is Used in Business Today 1. Marketing Marketers across different organizations are leveraging AI to enhance their ability to target and effectively grab the attention of their audiences. Nearly 75% of marketers use AI to perform their day-to-day roles, and over 68% of marketing leaders who invested in AI report seeing a positive ROI, according to our recent State of AI in Marketing report. Research by Meta also reveals that campaigns that use AI-powered tools have an average click-through rate 11% higher and a conversion rate 7.6% higher compared to those not using AI. Example of AI in Marketing I think a great example is how multinational beverage company Coca-Cola used AI to create the “Masterpiece ad” in 2023. Although the company collaborated with some contemporary artists — like Wonderbuhle, Aket, F. Ramadan, S. Tejada, and V. Kushwah — and used some live action shots in the video, its use of AI-powered image generation and motion synthesis played a key role in creating fluid transitions between the painting and the real world. The result? A video that sparked global conversations and drove engagement, highlighting how forward-thinking brands can stay relevant in a digital space that is fast evolving by using AI to push the boundaries of creativity. Pro tip: If you’re looking to start leveraging AI in your marketing strategies and are unsure of where or how to start, check out HubSpot’s AI Sales and Marketing tools to help improve your team’s performance. 2. Analytics One of AI’s biggest selling points is its ability to comb through vast amounts of data and provide insights relevant to your needs. It’s like it promises to find the proverbial needle in the haystack in 30 seconds flat. This makes it possible for companies to consider big data in real time and provide predictions that can inform decisions. AI-powered analytics are transforming industries across the board. From retail to finance, banking and more, all kinds of businesses are now using AI to analyze the behaviors of their customers and what drives those behaviors. And that helps these businesses to create more tailored solutions that effectively serve customer needs. Examples of AI Analytics Did you know 80% of what we watch on Netflix are suggestions provided based on its analysis of individual watch history? According to Netflix, this highly personalized recommendation engine saves the streaming company from losing at least $1 billion every year. The model continuously learns from user behavior, watch history, and engagement patterns, refining its recommendations to keep subscribers hooked. But Netflix isn’t alone. I love how Marks & Spencer (M&S) has implemented AI technology to enhance its online shopping experience by offering personalized style advice. Customers could complete a quiz detailing their size and style preferences, and the AI system, combined with insights from in-house stylists, provided tailored outfit suggestions. This initiative led to significant engagement, with 450,000 shoppers using the feature. In addition, M&S further increased automation of product descriptions to 80%, aiming to boost its digital fashion sales to 50% by 2028, up from one-third at the time. Pro tip: To maximize AI-driven analytics, focus on clean and structured data — even the most advanced AI models need high-quality inputs to deliver valuable insights. 3. Operations Operations form the backbone of any business, and AI is proving to be a game-changer in this domain by automating repetitive tasks and optimizing complex processes. In manufacturing, for example, AI-powered robotic process automation (RPA) handles tasks like assembly, quality control, and inventory management. In fact, installations of industrial robots were at an all time high (the second highest in history) as of 2023, according to the International Federation of Robotics. Beyond retail and manufacturing, AI-driven process automation is helping businesses optimize scheduling, streamline logistics, and improve operational resilience — allowing companies to do more with less. Example of AI in Operations I think Walmart’s AI-and-machine learning-driven inventory management systems is a brilliant use case of AI operations. It focuses on demand forecasting, reducing understock and overstock situations by at least 35%. That is millions of dollars saved every year! Pro tip: AI works best in operations when paired with human oversight. The key is to automate the repetitive while keeping decision-making in human hands. 4. Customer Service and Support And then there’s the belle of the ball — the customers and how they are supported throughout their journey. Customers want faster and more personalized service, and integrating AI always gets this job done. Our State of Service report found that: 84% of customer support reps see AI as an instrumental tool for interacting with modern customers. 92% say that AI has improved their customer service response times. 77% believe that AI will handle most ticket resolutions by 2025. Beyond chatbots, AI-powered sentiment analysis also allows businesses to detect customer frustrations

When HubSpot’s Global Events Team had its first kickoff planning session for INBOUND 2024, they weren’t sure about the creative direction they’d take. One thing was clear though: Given how huge AI has become in the last few years, they knew they’d incorporate it somehow. I connected with the team to learn more about the important role AI played in pre-production – from creating eye-catching visuals with Midjourney to targeted agendas with Claude. Creating Fresh Interstitials Prior to INBOUND 2024, the team kept the onsite interstitials pretty consistent year-over-year, sticking to static visual imagery and light animation. It was time for a change. Instead of graphics that would disappear into the background, they wanted to create interstitals attendees would want to engage with. “This year, AI has been big, and we got an intro to Midjourney and the work that Eduardo had been doing with the tool,” said Sav Aaver, former HubSpot production manager who led the collaboration. “We decided to look into that to add more interest to our screens, create a new element so people have something to look at that sparks conversation, if they’re sitting and waiting and working between sessions.” The Global Events team tapped Eduardo Garcia-Lopez, who leads HubSpot’s Visual Design team, to oversee the production of fresh interstitials with AI – leveraging Midjourney to generate flat, 2D images and Runway for video and animation. They weren’t trying to create something net new, he says, but they wanted to push their branding. “We didn‘t want it to feel like, ‘Oh yeah, this is very on the brand. It’s exactly what we would expect,’” Garcia-Lopez said. “We wanted to explore, because AI gives us these possibilities – you can take it anywhere you want but still maintain the same style of the brand.” After some brainstorming, they settled on three themes: An umbrella theme for INBOUND, which was very abstract and closely tied to the HubSpot brand. An extended brand for each stage. A third, surreal, abstract city theme The Creative Process with AI From start to finish, it took the team roughly a month and a half to generate 28 final videos, with 14 minutes of run time adapted for seven screen types at the venue. Starting with a baseline of shapes, gradients and colors that have always been the foundation for INBOUND graphics, Garcia-Lopez fed those initial images to Midjourney. That allowed him to generate initial ideas and options to present to the Global Events team. With AI, the person doing the prompting becomes the director, he says. “You‘re telling the AI. ‘Here’s my vision. Now, go out and do that,’” he says, “It takes a while, but if you compare that to a team, you still need somebody to drive the vision.” Once both teams agreed on the final 2D images for each stage and location, Garcia-Lopez headed to Runway for mini video shorts and animation. “It took me roughly two days to create the Boston mini paper city, generating all the flat images and creating a storyboard, going into Runway and animating all of them,” he says. “Then, I went into Adobe Premiere to edit the whole thing.” He also used Topaz, an AI editing software that enhances the quality of AI videos.  I wondered, how much would it cost if the team worked with a vendor to create these assets the old fashioned way.  Garcia-Lopez estimates requesting a 30-second clip would take at least two weeks and thousands of dollars. That said, Midjourney wasn’t exempt from the oddities that happen when you use AI to mimic reality. “What I presented to the team was the most presentable, the cleanest options, but behind the curtains, there were many generations that were not coming out well,” Garcia-Lopez says. “It was very choppy. You would have something weird happen – buses going into each other, buses running into people, like all these weird things.” What took a lot of time was identifying the best takes and cleaning up inconsistencies, he says. Thankfully, AI works fast. “We were iterating in a matter of days. We’d have new options in a couple hours, so it was very, very fast,” Garcia-Lopez said. “As a designer, we would’ve needed a big team to deliver all of these assets, even an illustration for all the variation of styles.” With Midjourney’s assist, he was able to create 25 distinct styles – a result he calls “almost unthinkable” in the time they had. “You need a big team with specific skills to accomplish a specific style,” he says, “and we were able to go wild and choose what we wanted.” AI isn’t without its limitations. When I asked Garcia-Lopez about the design challenges that come with leveraging AI, he said there’s a big one people often forget. “Editing something is actually quite hard. With an editable file, like a vector-based design in Adobe Illustrator, you can change every little detail,” he says. “With Midjourney and these AI tools, it’s not that easy.” It’s a myth that editing with AI is quick, he says. For example, color wise, you might not always get the exact same colors you’d achieve from a color palette. But you can get something pretty close. Aaver echoes that sentiment. “It was a big learning experience for us, as the approvers and reviewers,” she said, “learning what we can and can‘t give feedback on, what’s an easy change, what’s not so easy.” In addition, AI isn’t doing the bulk of the work, contrary to popular belief. There is a lot of bringing it back to Adobe and then feeding it back into the AI model to get the results that you want., Garcia-Lopez says. “You can do a lot with a small team, but you need skills in other software and a good background to solve a lot of these issues,” he adds. Creating Custom Agendas for Attendees In addition to interstitials, the Global Events team also turned to Anthropic’s Claude to create assets. “We wanted to

We turned our standard nurture email flow into an AI-driven conversion powerhouse. Here’s what we did, what worked (and what didn’t), and what we learned along the way. When our marketing team began discussing how to strategically incorporate AI into our workflows, we knew we wanted big results. But here’s the thing about big results: They don’t come from trying everything at once. With limited resources and unlimited possibilities, we needed to hone in on which AI applications would deliver the biggest impact. Email marketing seemed like a natural starting point for us. We’d been running optimization tests on our nurture flows for years, but after a while, the gains became incremental by a few percentage points here and there. We needed something that was a total game-changer. Something that had both meaningful influence on top-of-funnel metrics and practical usability across our marketing team. But what — and how? In a recent Marketing Against the Grain episode, HubSpot VP of Marketing Emmy Jonassen and I share how we experimented with AI to transform our email performance. We’ll also explain how we achieved an 82% increase in conversion rates — plus, all the lessons we learned along the way. Identifying the Challenge First, let me explain what we were doing before AI. Like most marketing teams, we approached email personalization through segmentation — grouping leads based on similar characteristics, then tailoring content to those groups. For example, if someone downloaded marketing-related content, we’d send them more marketing resources rather than sales content. It wasn’t a bad approach. But it was essentially educated guessing at the group level. We were saying, “People like you typically want this,” rather than understanding what each individual person was trying to accomplish. We wanted to do better than that. The Hypothesis: Moving From Groups to Individuals The more we looked at AI’s capabilities, particularly its ability to analyze multiple data points and identify patterns, the more we saw a path to true one-to-one personalization at scale. So, we asked ourselves: What if AI could help us understand not just what group or cohort someone belongs to but also the specific job they’re trying to get done? For example, rather than sending marketing content to all “marketing people,” we wanted to be able to pinpoint when a specific marketing manager at a specific company is ready to build their influencer strategy for this specific upcoming product launch. From there, we could send them exactly what they need for that task. It was a tall order … but we were willing to give it a try. The Setup: Building our AI Solution To test our hypothesis, we first designed a process that would enable AI to do what humans can’t: Analyze thousands of individual user intents at scale and craft tailored recommendations. Here’s how the process worked. When someone fills out a form to download HubSpot content, we collect a few key pieces of information: their business URL, company size, and what content they’re interested in. While these might seem like basic data points, they’re actually the foundation of understanding someone’s goals. Our AI system then takes these inputs and runs through a specific process: First, we analyze their business website to understand what their company does. We look at which content offer they downloaded and any other actions they’ve taken on our site. Our AI creates a detailed summary of what this person is likely trying to accomplish. The system then imagines the perfect piece of content to help them — whether or not it exists in our library. That “perfect” content gets compared against our actual content library using a vector database to find the closest matches. Finally, the AI crafts a personalized message explaining exactly how the selected content will help them achieve their specific goal. After this system was in place, it was time to step back and see if it would actually work. Testing, Failing, Learning, and Iterating If there’s one thing we learned quickly, it’s that AI doesn’t nail it on the first try. Our initial attempt focused on optimizing the email copy to make it more personalized and engaging. The results? Meh. This was our first big learning: The real ‘magic’ isn’t in the email itself but in how well the AI could predict what the user actually needed. So, we went back to the drawing board. We refined the AI’s training data, improving its ability to interpret user behavior and guess the job-to-be-done. We tested. We iterated. And, after months of tweaking and adjusting, we finally hit the sweet spot. The Results and Practical Outcomes The results surprised even us: +82% increase in conversion rate +30% boost in open rates +50% increase in click-through rates These numbers are impressive — but what is even more astounding is what it looks like from a practical standpoint. For example, during the experiment, our AI analyzed a lead from an organic cold brew coffee company who had downloaded influencer marketing resources. The system noticed they’d recently shown interest in content planning and organization, particularly as winter approached. From this behavior pattern, AI deduced they were likely preparing for seasonal promotions or new product launches. Instead of just offering generic marketing content, the system recommended our content strategy course with copy specifically tailored to their business. Here’s what we sent: “Turn every sip into a story that captivates and converts.” This level of personalization — understanding both their business context and immediate goals — is what drove our dramatic improvement in results. The Power of AI What made this experiment truly remarkable was proving that AI could move us beyond basic personalization (“Here’s some content for marketers”) to true personal connection (“Here’s exactly what you need for your specific company’s specific campaign”). While not everyone has HubSpot’s content library or technical resources, the core lesson stands: AI’s real power in marketing isn’t just automation — it’s understanding individual customer needs at scale. 5 Tactical Tips for Incorporating AI into Your Marketing Strategy While