How Leading Companies Are Using AI in Marketing (2025)

Introduction

When I think about how AI is changing marketing, it’s like watching a new chapter unfold. Everywhere I look, LinkedIn posts, podcasts, or even just a quick search, there’s a real story of how brands are using AI to sharpen their campaigns and actually connect with people. 

And it's not just noise. A McKinsey report found that 71% of high-growth companies are already using gen AI in at least one area of their business. That’s a clear signal that AI isn’t a trend. It’s becoming the foundation for modern marketing.

As Vijay Chittoor, CEO of Blueshift, says:  “Customer experience is the new battlefield for competitive advantage. In the AI-first world, the only survivors on this battlefield will be the ones who embrace AI at the core of their marketing and customer experience strategies.”

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In this blog, I’m going to share some of the best examples of companies using AI to transform their marketing strategies and show how you can use AI to save time, boost conversions, and drive more revenue.

5 Leading B2B Companies Leveraging AI for Marketing

Here’s a quick look at five B2B companies that are putting AI to work in their marketing efforts. From smarter campaigns to improved customer engagement, these examples show how AI is transforming B2B marketing in practice. 

1. Klarna

The Challenge: High Costs and Inefficient Content Production

Klarna, a leading fintech company, faced escalating marketing expenses, particularly in producing bespoke images and managing multiple campaigns for various retail events. Traditional methods were time-consuming and costly, hindering the company's ability to respond swiftly to market trends

The Solution: Implementing Generative AI Tools

To address these challenges, Klarna integrated generative AI tools such as Midjourney, DALL·E, and Adobe Firefly into its marketing operations. These tools enabled the company to automate image creation and streamline content production processes.

Klarna CMO David Sandström says: "AI is helping us become leaner, faster, and more responsive to what our customers care about, leading to a much, much better experience.

Implementation and Current Scenario

• Image Production: In the first quarter of 2024, Klarna generated over 1,000 marketing images using AI, reducing the image development cycle from six weeks to just seven days.
  
  
• Campaign Management: The company launched nearly 30 AI-generated marketing campaigns tied to events like Mother's Day and Black Friday, focusing on high-volume, time-consuming tasks.
  
  
• Operational Efficiency: AI tools facilitated frequent updates to the company's app and website visuals, tailored to various retail events, without incurring high costs.

The Impact: Significant Cost Savings and Enhanced Productivity

• Cost Reduction: Klarna achieved annual savings of approximately $10 million by incorporating AI into its marketing operations. Specifically, the company saved $6 million in image production costs and $4 million by reducing reliance on external marketing suppliers. 
• Increased Efficiency: The use of AI allowed Klarna to run more marketing campaigns while simultaneously reducing the sales and marketing budget by 11% in the first quarter of 2024, with AI accounting for 37% of the cost savings.
• Enhanced Customer Experience: By leveraging AI, Klarna improved its responsiveness to market trends, delivering timely and relevant content to customers, thereby enhancing the overall customer experience.

Klarna's strategic implementation of generative AI in marketing demonstrates the transformative potential of AI technologies in enhancing operational efficiency, reducing costs, and improving customer engagement.

2. Artesian Solutions

Artesian Solutions, a UK-based sales insights company, was facing a familiar challenge: how to provide seamless, round-the-clock engagement for a diverse audience of decision-makers, from heads of sales to COOs. Buyers expected informed, relevant answers at any time, making traditional approaches to customer service and sales engagement fall short.

The Challenge: Meeting Rising Customer Expectations

The current scenario demanded an innovative solution. Artesian needed a way to not only meet these expectations but also personalize interactions and gather better insights to build stronger relationships.

The AI Tool: Conversational AI Platform (CAP)

To tackle this, Artesian deployed a Conversational AI Platform (CAP) named Arti. Designed as a chatbot with a human touch, Arti was built to handle complex sales queries, personalize conversations based on user data, and act as an intelligent lead-generation tool.

The Impact: Transforming Customer Engagement

The impact was impressive. In its first month, Arti was involved in over 1,000 sessions, engaging with more than 750 unique visitors from 60 different organizations. Over the next year, it boosted monthly website traffic by 11%, answered more than 5,000 questions with 99.1% accuracy, and expanded Artesian’s prospect pool by four times.

Arti’s success highlighted how AI can revolutionize B2B customer engagement and make a real difference in sales performance.

3. VMware

The Challenge: Scaling Content Without Compromising Quality

In 2015, VMware was rapidly expanding, but its content operations were struggling to keep up. With 120 technical writers producing over 400 content releases a year, the editorial team, just five people, could only review about 1% of all output. Team churn and reliance on freelancers made consistency a challenge, creating a real risk of substandard content that could damage VMware’s reputation.

At the same time, VMware’s product release cycles were accelerating, sometimes requiring twice-daily updates. It became clear to Laura Bellamy, VMware’s Director of Information Experiences, that traditional workflows weren’t going to cut it anymore.

The Solution: Integrating AI with Acrolinx

Bellamy found a solution in Acrolinx, an AI-powered content platform. Acrolinx automated repetitive editing tasks, freeing up VMware’s editors to focus on high-value activities like taxonomy, classification, and training. It also provided real-time dashboards and quantitative quality reports that are essential for making quick decisions in a fast-paced environment.

The Impact: Efficiency, Quality, and Visibility

The results spoke for themselves. Acrolinx helped VMware’s editors shift from basic editing to strategic tasks, improving content quality at scale. According to internal surveys, 73% of VMware employees reported improved content quality, while 60% said they became more efficient creators.

Beyond speed and efficiency, Acrolinx also reduced the risk of publishing substandard content. Bellamy noted that the platform ensured a baseline of quality for all output, even in emergency scenarios where content needed to be produced and published in a single day.

Acrolinx’s analytics capabilities gave VMware the ability to track content performance across business units and demonstrate ROI on content investments that are vital for securing continued budget support.

Looking Ahead

With Acrolinx, VMware is now tackling the next big challenge: refining its content’s tone of voice to improve customer experience further. Using data from Acrolinx, Bellamy’s team is making informed decisions about content direction and enabling greater self-service for other teams. Meanwhile, Acrolinx is continuing to evolve its AI capabilities, aiming to meet the growing demands of B2B marketers under pressure to be faster and better than ever.

4. Atlan 

Objective: Establish Atlan as a Subject Matter Expert and Boost Organic Visibility

Atlan wanted to position itself as a go-to resource in the modern data stack space. However, despite having the right insights, scaling content production without sacrificing quality was proving to be a challenge. They needed a system that could help them create high-quality content consistently, optimize for search, and capture a larger share of organic traffic.

The Challenge: Scaling Without Sacrificing Quality

Atlan’s goal was clear: dominate SERPs for high-intent, data-driven topics. But scaling up content creation while maintaining expert-level insights and SEO alignment was a bottleneck. Without a scalable system, producing content that could consistently meet the needs of technical and business audiences was impossible.

The Solution: Custom AI-Driven Content Engine

To address this challenge, Atlan partnered with Revv Growth to build a custom content engine for Atlan, combining GPT-powered prompts with human editorial expertise. This approach allowed us to:

• Develop detailed, SEO-driven blog outlines by reverse-engineering SERP competitors.
  
  
• Use custom-trained GPT prompts to generate first drafts, ensuring content is structured, on-brand, and covers user intent.
  
  
• Blend AI drafts with human editorial rewrites, adding context, real-world examples, and credible data sources (post-2023 references like McKinsey, Gartner, and industry surveys).
  
  
• Ensure brand tone and clarity with style guides and formatting best practices.
  
  
• Optimize every blog using tools like Clearscope, ensuring semantic richness and search engine competitiveness.

Why It Worked: Combining AI, Human Expertise, and SEO

This AI + Human hybrid approach helped us:

• Deliver consistent quality and publish at scale without overloading Atlan’s internal team.
  
  
• Maintain factual accuracy and brand alignment by leveraging human editors to polish AI drafts.
  
  
• Build a process that is agile, data-driven, and repeatable, which is ideal for SEO and long-term content growth.

Results: From 117K to Market Leadership

Since deploying this system, Atlan’s organic traffic has grown from 117K to consistently outperforming in its niche. Today, most of Atlan’s blogs rank in LLM summaries or hold the top spot on SERPs. Our content engine delivered many high-quality, long-form blogs within two months, each meticulously optimized for search and aligned with Atlan’s SME positioning.

5. Ingersoll Rand

The Challenge: Breaking Free from Generic Campaigns

Ingersoll Rand’s HVAC division knew it had a wide variety of customers like HVAC technicians, self-servicing owners, and more, each with distinct information needs. However, their marketing was stuck in generic campaigns, failing to resonate with these unique segments. The team, led by Melanie Fox, realised they needed to break away from “batch-and-blast” campaigns and instead build localised, highly-targeted messaging. The challenge was doing this at scale, without adding to the team’s workload.

The Solution: AI-Driven Marketing Automation with IBM Watson

Fox’s team turned to IBM’s Watson Campaign Automation platform. Unlike traditional marketing automation tools, this AI-powered platform offered a fundamentally different approach. Watson’s AI capabilities streamlined time-consuming tasks like segmentation and dynamic content personalisation, enabling the team to deliver hyper-relevant campaigns quickly.

By combining Watson Campaign Automation with IBM’s WeatherFX solution, Ingersoll Rand could also factor in real-time local weather data. This meant campaigns could adapt to events like hailstorms or flooding, proactively prompting customers to inspect or upgrade their HVAC systems.

The Impact: More Relevant Campaigns, Better Results

The results have been significant:

• Dynamic Personalisation at Scale: Using AI, Ingersoll Rand could reuse campaign templates but tailor content to customer roles and localised needs.
  
  
• Higher Engagement: Campaign metrics such as open rates, click-throughs, and conversions have seen notable improvements.
  
  
• Contextual Relevance: Weather-related triggers meant campaigns were more timely and actionable, building trust with customers in affected regions.

Fox says this AI-driven shift has helped them build stronger, long-term relationships with both prospects and existing customers, driving engagement and loyalty.

Looking Ahead: A Future of Cognitive Marketing

Ingersoll Rand’s AI journey is still in its early stages, but Fox is excited about what’s next. She sees opportunities for AI to go beyond campaign execution, using cognitive insights to suggest entirely new audience profiles and campaign ideas. The ultimate vision? A marketing AI assistant akin to Jarvis from Iron Man, proactive, intelligent, and seamlessly integrated into the team’s workflows.

How Are B2B Companies Using AI in Marketing?

B2B companies are leveraging AI like never before, transforming complex data into informed decisions and more targeted campaigns. From predictive analytics to dynamic content creation, AI is helping marketers cut through the noise and drive real impact in once-impossible ways.

1. Predictive Lead Scoring & Qualification

Let’s start with predictive lead scoring, one of the most popular uses of AI in B2B marketing. If you’ve ever felt overwhelmed by a mountain of leads and wondered, “Who’s actually ready to buy?” This is where AI steps in.

AI-powered predictive lead scoring tools analyze massive datasets, including website interactions, email opens, content downloads, and social media activity. They can then assign a “likelihood to convert” score to each lead. This takes the guesswork out of prioritization, helping sales teams focus on the most promising prospects.

For instance, Tradera, a subsidiary of PayPal, increased its gross revenue by 125% on its website through predictive recommendations, showcasing how predictive AI can deliver results, no matter the industry.

2. AI-Driven Account-Based Marketing (ABM)

Next up is AI-powered Account-Based Marketing (ABM). In the old days, ABM meant manually researching and crafting campaigns for target accounts that were time-consuming and hard to scale. But AI has completely transformed the game.

At Revv Growth, we’ve seen firsthand how AI-driven ABM can deliver incredible results. One standout example is our work with Vymo, a Series C-funded SaaS platform targeting Indian banks and insurance companies. Vymo came to us with an ambitious goal: build a $3 million quarterly pipeline in a highly competitive market.

At Revv Growth, we’ve seen firsthand how AI-driven ABM can deliver incredible results. One standout example is our work with Vymo, a Series C-funded SaaS platform targeting Indian banks and insurance companies.

Vymo came to us with a bold goal: build a $3 million quarterly pipeline in a fiercely competitive financial market. We knew this campaign needed precision targeting, personalized content, and strategic timing. And this is exactly where AI stepped in.

We started by using AI to analyze buying signals from website visits to form submissions and LinkedIn activity. By layering this behavioral data with firmographics, industry trends, and engagement history, we built dynamic profiles for each of Vymo’s 50 high-value target accounts.

With these insights, we were able to:

• Prioritize accounts based on real-time intent and engagement patterns
• Customize LinkedIn campaigns using AI-generated content recommendations
• Run hyper-targeted email sequences that adapt based on user behavior
• Automate retargeting workflows to re-engage visitors who showed interest
• Optimize campaign timing using predictive analytics to surface the best outreach windows

But we didn’t stop at digital. AI also helped us identify the most promising contacts for offline touchpoints, whether that was sending personalized invites to CXO roundtables or following up with tailored print assets. Everything was orchestrated to feel relevant and timely.

Here’s what we achieved:

• Marketing-Sourced Pipeline: $21 million generated through ABM and pipeline acceleration campaigns
  
  
• LinkedIn Campaign Impact: 500+ Marketing Qualified Leads (MQLs) delivered
  
  
• Increased Engagement: Dramatic uplift in content engagement across target accounts
  
  
• Stronger Sales-Marketing Alignment: Sales teams were equipped with AI-curated intel, leading to faster deal closures and higher-quality conversations

For me, this project perfectly captured what makes AI in ABM so powerful: it turns guesswork into guided action. Instead of manually chasing leads, we let AI do the heavy lifting, helping us show up with the right message, at the right time, for the right people.

3. Conversational AI & Chatbots for B2B Lead Nurturing

When it comes to engaging prospects and customers, conversational AI is rewriting the rules of the game. AI chatbots and virtual assistants can handle routine tasks, like answering FAQs, qualifying leads, and booking meetings, 24/7.

A great example of this in action is Artesian Solutions, a UK-based sales insights company. Artesian faced the challenge of engaging decision-makers across various industries: people who expect informed, relevant answers whenever they reach out, whether it’s a minor query or a major deal in the making. To meet this need, Artesian teamed up with AI-specialist agency Volume to create a conversational AI platform called Arti.

Arti was designed to do more than just answer questions. It acted as an intelligent, always-on resource that could handle complex sales queries, personalize conversations based on user data, and deliver the level of responsiveness today’s B2B buyers expect. 

From day one, Arti became a powerful tool for nurturing leads and building deeper relationships with decision-makers. For me, this example highlights just how game-changing AI can be for B2B marketing that builds trust and accelerates deals.

4. Advanced Marketing Analytics & Insights

One of the biggest superpowers of AI is its ability to turn raw data into real-time, actionable insights. Instead of simply tracking clicks and impressions, AI-powered analytics reveal exactly what’s working, what’s not, and where you should double down.

For instance, ServiceMax partnered with Demandbase to harness AI’s predictive capabilities. Instead of guessing what content would engage each visitor, they used AI to map out the next best steps in real-time, like having a digital concierge guiding visitors to the most relevant content.

The impact was dramatic: bounce rates dropped by 70%, while time-on-site and pages-per-session more than doubled. That’s the kind of precise, data-driven optimization that transforms B2B marketing from a guessing game into a results-driven machine.

This example perfectly illustrates how AI analytics don’t just measure performance, they actively shape it. By using AI to predict and personalize the buyer journey, you can engage your audience more deeply and boost conversion rates without adding more manual effort.

5. Content Generation & Personalization at Scale

Another area where AI shines is content generation and personalization. Gone are the days when creating personalized messaging for each segment felt impossible. With AI, you can create content that speaks directly to your audience at scale.

Take LinkedIn, for example. LinkedIn uses AI to personalize job recommendations, content suggestions, and event invitations for every member on the platform. It’s like having a team of data scientists working around the clock to make sure every interaction is as relevant as possible.

As Deepak Agarwal, VP of Artificial Intelligence at LinkedIn, puts it:

“At LinkedIn, AI is like oxygen. We’ve been using it for over a decade to create the member experiences that people value most on our platform.”

One example of how this works in practice is the work we did with Everstage, a global sales compensation software platform. They wanted to scale their content output quickly, without sacrificing quality or consistency, so we designed a comprehensive AI-powered content workflow just for them. Here’s what that looked like:

• Deep SERP Analysis: We identified what their audience, like RevOps and SalesOps leaders, was actively searching for and where competitors were falling short.
  
  
• AI-Driven Content Framework: We created prompts for every step, such as outlining, stat sourcing, first-draft writing, and FAQs, so no one had to start from scratch.
  
  
• Human Editorial Touch: Our team then stepped in to refine tone, add real-world insights, and make sure every blog and email sounded exactly like Everstage: authoritative, data-driven, and engaging.
  
  
• SEO and Visual Optimization: We design and embed original images or use chart-based data visualizations from reputable sources like McKinsey, Gartner, or Bridge Group. These images aren’t just decorative, they’re selected or created to add context, support claims, and improve readability. Every piece is also fine-tuned for SEO with metadata, internal linking, and accessibility-compliant alt text.

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The result? Our client’s blogs didn’t just start ranking on search engines, they also came up on  ChatGPT responses cited as a source. It helped them stand out as thought leaders in their space. 

And just as importantly, we broke free from endless content creation cycles using AI to handle the heavy lifting while our team added the creative, human touch that brought everything to life.

Benefits of Implementing AI in B2B Marketing

Over the past few years, I’ve seen firsthand how AI can turn B2B marketing from a shot in the dark into a precisely targeted, data-driven powerhouse. When done right, AI can completely transform how we connect with customers and drive revenue. Here are some of the most significant benefits I’ve noticed:

1. Enhanced Personalization at Scale

Personalization has always been tough in B2B: you could either target a few accounts really well or settle for one-size-fits-all messaging. But AI changes that game. McKinsey found that companies using AI-powered personalization see up to 40% more revenue than those sticking to generic messaging.

I’ve seen this work wonders with Adobe’s personalization engine. For example, the Royal Bank of Scotland used AI to increase engagement by 20% and cut down on customer churn.

2. Improved Lead Qualification

Lead qualification can make or break your marketing efforts. I remember the days when sales and marketing teams spent hours debating which leads were worth pursuing. But with AI, you don’t have to guess anymore. A Salesforce study found that 79% of high-performing marketers use AI to automate lead qualification and nurturing, boosting efficiency and pipeline quality.

Take 6sense, for example. They use AI to identify buying intent and prioritize high-value accounts, ensuring reps focus on leads that convert. With their AI-powered SDR, Piper, they dynamically engage website visitors based on behavior and segment, pre-qualifying leads before passing them to sales. This shift helped 6sense double its opportunities in just one quarter, turning lead qualification from a manual grind into a revenue engine.

3. Data-Driven Decision Making

Gut instinct alone doesn’t cut it in modern marketing. That’s why I’m a huge fan of how AI brings data-driven decision-making to the forefront. AI-powered analytics unlock insights about customer behavior, campaign performance, and what’s resonating with your audience.

A great example is LinkedIn’s Account Prioritizer, which uses AI to analyze data points and forecast account growth potential. According to LinkedIn, this tool has delivered an 8.08% increase in renewal bookings, a powerful testament to how AI insights can directly impact revenue.

Challenges and Considerations

While the potential of AI in B2B marketing is huge, it’s not without its challenges. Over the past few years, I’ve seen companies excited to jump in, only to get tripped up by these common hurdles. Here’s what you should keep in mind as you adopt AI in your marketing stack.

1. Data Privacy and Compliance

As marketers, we’re handling more data than ever. Some of it is sensitive, much of it regulated. And when you’re using AI to process and act on that data, you need to be doubly sure you’re staying compliant. From GDPR in Europe to CCPA in California, there’s a patchwork of regulations to navigate. It’s not just about avoiding fines, it’s about building trust. No customer wants to feel like their data is being used in ways they didn’t agree to.

A 2025 McKinsey Global Survey found that respondents at larger organizations are much more likely to say their organizations are actively managing cybersecurity and privacy risks tied to their AI use. This underscores the importance of building robust data governance frameworks and safeguarding data privacy to protect customer trust and avoid costly pitfalls.

2. Integration with Existing Systems

Integrating AI into existing marketing infrastructures presents a significant challenge for B2B companies, many of which operate with complex ecosystems comprising CRM, marketing automation, and data analytics tools. AI tools yield the best results when seamlessly integrated rather than functioning in isolation. Achieving this often necessitates customizing APIs and reconfiguring data pipelines to ensure cohesive operation. A robust data architecture is crucial; without it, AI insights may be limited or misleading.

This challenge is widespread. According to a DemandGen survey, 43% of B2B marketers identified data integration issues as a significant hurdle in implementing AI within their organizations. These integration difficulties can impede the effectiveness of AI tools, underscoring the importance of a well-structured and unified data system.

3. Ensuring Data Quality

AI is only as good as the data it learns from. If your data is outdated, incomplete, or inconsistent, you’re setting yourself up for flawed predictions and missed opportunities. In fact, a 2024 McKinsey survey found that 70% of top-performing companies struggled to integrate data into AI models, often because of issues with data quality, governance, and insufficient training data

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To truly harness the power of AI, you need to treat data quality like a first-class citizen. This means investing in regular audits, data hygiene practices, and clear governance. It might not be as glamorous as a shiny new chatbot or personalization tool, but it’s the foundation that makes everything else work.

Best Practices for Implementing AI in B2B Marketing

After seeing how AI has transformed B2B marketing for companies big and small, I’ve come to believe that success isn’t just about having the right tools; it’s about how you use them. Here are some best practices that can help you unlock the full potential of AI in your marketing efforts.

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1. Establish a Dedicated AI Adoption Team

Having a dedicated team, like a project management office for AI adoption, ensures consistent ownership and accountability. This team acts as the backbone for successful implementation, driving alignment across marketing, sales, and customer teams.

2. Foster Regular Internal Communication

Transparent communication about the value of AI creates buy-in across departments. Regular updates and sharing success stories help everyone understand how AI enhances marketing campaigns and improves outcomes.

3. Engage Senior Leadership

When senior leaders are actively involved in AI adoption, it signals that AI is a strategic priority. Their sponsorship helps secure budgets, accelerate change, and build a culture that’s open to AI-driven transformation.

4. Offer Role-Based Training

AI is only as effective as the people using it. Providing role-specific training from marketers to data analysts ensures everyone has the skills to leverage AI effectively and integrate it into their day-to-day work.

5. Integrate AI into Business Processes

Embedding AI tools into existing marketing workflows like lead scoring, content personalization, and campaign optimization makes AI adoption feel natural. It’s not a separate project; it’s how work gets done.

Future Trends in AI for B2B Marketing

If there’s one thing I’ve learned about AI in marketing, it’s that it never stands still. The tools and strategies we’re using today are just the beginning; new trends are already shaping the next wave of AI-powered marketing. Here are four that I’m especially excited about:

1. Voice Search and Conversational AI

Voice search isn’t just for B2C anymore. As more decision-makers rely on voice assistants and smart devices, B2B marketers need to think about how voice search fits into their strategy. 

Imagine a busy executive asking their virtual assistant for insights about your brand or comparing vendors. If your content isn’t optimized for voice queries, clear, conversational, and focused on real questions, you’re missing out.

In fact, as per a McKinsey report, in mature AI-driven customer service models, more than 95% of service interactions and requests can be solved via digital and straight-through-processing (STP) channels. This stat is a powerful reminder that today’s B2B buyers expect fast, accurate responses, 24/7. 

2. AI-Powered Account-Based Marketing

Account-Based Marketing has always been about precision targeting, but AI is taking it to the next level. Instead of generic ABM campaigns, we’re seeing AI identify subtle buying signals and trigger hyper-personalized outreach. In fact, companies implementing ABM strategies have reported a 75% increase in average deal size and a 150% boost in customer lifetime value through targeted upsell campaigns as per a Gartner report. 

For example, AI tools can now analyze signals like intent data, engagement across different touchpoints, and even competitive activity to recommend the best actions for each account. This means your ABM efforts can feel less like marketing and more like a one-to-one conversation.

I’m particularly intrigued by how AI-driven ABM platforms are helping marketers predict what kind of content or messaging will resonate with each decision-maker, before they even visit your website. It’s a powerful shift that’s turning ABM into a dynamic, data-driven engine of growth. And in my experience, the companies that embrace this approach aren’t just winning more deals, they’re building relationships that last.

3. AI-Driven Video Marketing

Video has always been a powerful way to tell stories, but AI is making it easier and more effective than ever. A DemandGen report states that marketers are seeing the highest ROI (71%), engagement (66%), and lead generation (60%) from their short-form video efforts, which really highlights just how impactful these quick, focused pieces of content can be.

AI tools help create personalized video content at scale, from dynamic video ads to tailored explainer videos. Take Synthesia, for example. They’re using AI to generate video content that can be localized, translated, and personalized in real time.

I see this trend accelerating as B2B marketers look for new ways to connect with busy decision-makers who’d rather watch a quick video than read a long whitepaper. And thanks to AI, these videos are becoming more engaging, more targeted, and more effective at every stage of the customer journey.

Let’s Put AI to Work: Your Next Step

Looking at all these examples, one thing is crystal clear: At the end of the day, AI isn’t about replacing marketers, it’s about freeing them up to do what they love.

But here’s what I’ve learned. It’s not about chasing shiny tools or trendy jargon. It’s about weaving AI into your marketing DNA in a way that aligns with your brand and your customers. When you see AI as a partner, not a replacement, that’s when the magic happens.

At Revv Growth, we’ve helped dozens of B2B companies use AI to sharpen campaigns, win over tough markets, and deliver outcomes that move the needle. And we’re just getting started.

So wherever you are in your AI journey, whether you’re dipping your toes in or diving headfirst, we’re here to help you make AI work for you, not the other way around. If you’re ready to transform your marketing with AI and see real-world impact, let’s chat.

Book a call with us today, and let’s build the future together.