Trends in AI for B2B Startups

Trends I'm currently observing in B2B SaaS: 1. Everyone and their mother is rushing to get their product AI-powered. Or is it AI-solution? Or AI-platform? Pick your poison. 2. AI features often require changes in customer behavior to be fully effective. Self-serve experiences struggle to accommodate these shifts, making human involvement frequently necessary. Sales and Support teams - it is your time to shine! Is PLG dead then (again)? Nah. But it's not always the preferred path. 3. There’s little understanding of how much AI actually deteriorates margins because... AI is so expensive and SaaS businesses are not used to deal with such costly 'things'. Move over astronomical AWS bills; there's a new, costly AI kid in town. 4. Freemium and free trials are hard to justify with AI costs. Paid and credit card–required trials are making a big comeback. 5. As AI costs change, companies will need to adjust their pricing... a lot. Having a platform that allows pricing experimentation will be key to success. 6. Product teams will need to get hands-on in owning pricing models for their features. SaaS is no longer 80%-margin candyland. Product teams will need to control costs and play a much more active role in pricing and packaging. 7. The rush to AI means many products will offer overlapping features, making it difficult to stand out. PMM-I feel your pain already... 8. Too many products are rushing to ride the AI wave by acting as simple ChatGPT wrappers with no proprietary functionality. This positions OpenAI (and others) to use these products as proof of concept, allowing them to observe user behavior (while getting paid!). Eventually, they can build competing functionality themselves and shut off API access whenever they choose. OpenAI giveth, and it can taketh away. 9. AI definitely feels reminiscent of the dot-com boom: exciting, but inevitably likely heading for a crash. #b2b #ai

AI is finally making services businesses scalable—and—exciting to VCs. The global services market is in the trillions of💰s, far larger than today’s software market. Yet, services businesses haven’t been the darlings of venture capital, as they were perceived to lack rapid scaling potential. 𝗔𝗜 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘁𝗵𝗮𝘁. By blending AI seamlessly with human expertise, there is an opportunity to get into much larger markets with models that have the potential to scale in ways services - or even SaaS businesses - can't. For example, instead of offering a marketing SaaS, an AI-powered Service-as-Software business can deliver what the customer really wants: high-quality leads or compelling content. We’ve seen this potential firsthand through Emergent Ventures’ investments in multiple AI-powered companies that leverage humans-in-the-loop. These models resonate with B2B customers because they offer faster, clearer paths to value—reliable outcomes delivered with greater efficiency. For many customers, it’s a significant upgrade over traditional agency or service-provider relationships. While the potential is huge, only a fraction of AI-powered services startups will scale. 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗱𝗲𝗽𝗲𝗻𝗱𝘀 𝗼𝗻 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗲𝗮𝗿𝗹𝘆 𝗰𝗵𝗼𝗶𝗰𝗲𝘀 𝗮𝗻𝗱 𝗲𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. Here’s what we have learned works well: 𝟭. 𝗔𝗜-𝗛𝘂𝗺𝗮𝗻 𝗦𝘆𝗻𝗲𝗿𝗴𝘆: AI and software should do the heavy lifting, with humans involved strategically— e.g. for validating AI output, edge cases, enabling adoption, or acting on AI insights. Over time, reduce human input as the AI learns, and models improve. Target 60%+ initial gross margins, with a path to SaaS-like 75%+ margins over time. 𝟮. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗛𝘂𝗺𝗮𝗻 𝗜𝗻𝘃𝗼𝗹𝘃𝗲𝗺𝗲𝗻𝘁: The dependency on hiring & training humans should not constrain scale and economics. Have a path to tapping into freelancers or agency partners. Leverage human experts in a high-talent location such as India. 𝟯. 𝗥𝗲𝗰𝘂𝗿𝗿𝗶𝗻𝗴 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: Focus on high-value, recurring use-cases to ensure subscription-based revenue with strong net revenue retention (NRR). 𝟰. 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗣𝗼𝘄𝗲𝗿: Iterate to a solution that can command higher pricing, and a model that aligns incentives with customers, e.g. based on outcomes. 𝟱. 𝗗𝗮𝘁𝗮 𝗠𝗼𝗮𝘁𝘀: Build solutions that improve with use, creating compounding competitive advantages over time. 𝟲. 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝗧𝗲𝗰𝗵: Architect a stack that can evolve with AI advancements. 𝟳. 𝗙𝘂𝗹𝗹-𝗦𝘁𝗮𝗰𝗸 𝗧𝗲𝗮𝗺: A founding team that has the technical expertise to build and rapidly improve complex AI-powered solutions, and deep operational acumen. A rare combination. These are complex businesses to build, and the right playbooks are yet to be perfected. But where this works, 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀-𝗮𝘀-𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗔𝗜 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲 𝗺𝗮𝗻𝘆 𝗕𝟮𝗕 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀 📈 #EnterpriseAI #startups #vc #SaaS

The biggest shift in adopting AI is actually a mental one. Humans are creatures of habit! There are so many patterns and behaviors that we're just used to and shifting our thinking and way of operating is hard. Here are 10 mental model shifts that B2B marketers need to make to fully embrace AI: 1️⃣ From ‘1:Many ABM’ to ‘1:1 ABM at Scale’ Traditional ABM was mostly 1:many given the manpower required to do 1:few or 1:1. AI lets you scale hyper-relevant, personalized messaging to each prospect and account, making ABM more about precision 1:1 engagement than just broad targeting. 2️⃣ From ‘One Big Campaign’ to ‘Always-On Micro-Campaigns’ Marketing campaigns often take months to plan and execute. With AI, marketers can continuously test, iterate, and personalize campaigns in real time, creating a network of smaller, targeted engagements based on "see intent, take action". 3️⃣ From ‘Copy as an Art’ to ‘Copy as a System’ The days of writing all copy from scratch are gone. With AI, marketers need to think of copy as modular components that can be generated, tested, and refined dynamically, optimizing for different contexts without manually rewriting everything. 4️⃣ From ‘Content Creation’ to ‘Content Orchestration’ Marketers have long focused on producing content, but AI enables them to shift towards orchestrating it—mixing, remixing, and repurposing existing assets dynamically for different channels, audiences, and formats. 5️⃣ From ‘Buyer Journey Stages’ to ‘Real-Time Adaptive Journeys’ Buyer journeys are no longer a linear funnel with predefined stages. AI allows marketers to personalize and adapt messaging in real-time based on each buyer’s behavior. 6️⃣ From ‘Content as Cost Center’ to ‘Content as Infinite Asset’ Before AI, content production was slow and expensive. Now, a single content asset (e.g., a webinar transcript) can be turned into blogs, social posts, email sequences, and more. AI makes content compounding possible at scale. 7️⃣ From ‘Marketing Team Execution’ to ‘AI-Powered Leverage’ Instead of viewing marketing as a set of tasks executed by a team, AI enables a leverage-based approach where a small team can generate the output of a much larger one. 8️⃣ From ‘SEO-Driven Content’ to ‘AI-Native Content Strategies’ SEO-driven keyword strategies are dying. Companies now have to adapt to creating content that can be ingested, interpreted, and surfaced by LLMs. 9️⃣ From ‘Personalization as Feature’ to ‘Personalization as Default’ Personalization used to be an expensive, resource-heavy add-on. Now, AI enables 1:1 personalization at scale, shifting personalization from a bonus feature to a ubiquitous one. 🔟 From ‘AI as Efficiency Tool’ to ‘AI as Strategic Partner’ Most marketers start using AI to speed up existing workflows, but the real shift happens when AI is a strategic collaborator and thought partner—suggesting campaign ideas, identifying opportunities, and challenging assumptions. What did I miss?

AI is no longer just an experimentation tool. It’s reshaping the entire optimization landscape. With this shift comes many untapped opportunities. Working with Andrius Jonaitis ⚙️, we've put together a growing list of 40+ AI-driven experimentation tools ( https://lnkd.in/gHm2CbDi) Combing through this list, here are the emerging market trends and opportunities you should know: 1️⃣ SELF-LEARNING, AUTO-OPTIMIZING EXPERIMENTS 💡 Opportunity: AI is creating self-adjusting experiments that optimize in real-time. 🛠️ Tools: Amplitude, Evolv Technology, and Dynamic Yield by Mastercard are pioneering always-on experimentation, where AI adjusts experiences dynamically based on live behavior. 🔮 How to leverage it: Focus on learning and developing tools that shift from static A/B testing to AI-powered, dynamically updating experiments. 2️⃣ AI-GENERATED VARIANTS 💡 Opportunity: AI can help you develop hypotheses and testing strategies. 🛠️ Tools: Ditto and ChatGPT (through custom GPTs) can help you generate robust testing strategies. 🔮 How to leverage it: Use custom GPTs to generate test ideas at scale. Automate hypothesis development, ideation, and test planning. 3️⃣ SMARTER EXPERIMENTATION WITH LESS TRAFFIC 💡 Opportunity: AI-driven traffic-efficient testing that gets results without massive sample sizes. 🛠️ Tools: Intelligems, CustomFit AI, and CRO Benchmark are pioneering AI-driven uplift modeling, finding winners faster -- with less traffic waste. 🔮 How to leverage it: Don't get stuck in a mentality that testing is only for enterprise organizations with tons of traffic. Try tools that let you test more and faster through real-time adaptive insights. 4️⃣ AI-POWERED PERSONALIZATION 💡 Opportunity: AI is creating a whole new set of experiences where every visitor will see the best-performing variant for them. 🛠️ Tools: Lift AI, Bind AI, and Coveo are some of the leaders using real-time behavioral signals to personalize experiences dynamically. 🔮 How to leverage it: Experiment with tools that match users with high-converting content. These tools are likely to develop and get even more powerful moving forward. 5️⃣ AI EXPERIMENTATION AGENTS 💡 Opportunity: AI-driven autonomous agents that can run, monitor, and optimize experiments without human intervention. 🛠️ Tools: Conversion AgentAI and BotDojo are early signals of AI taking over manual experimentation execution. Julius AI and Jurnii LTD AI are moving toward full AI-driven decision-making. 🔮 How to leverage it: Be open-minded about your role in the experimentation process. It's changing! Start experimenting with tools that enable AI-powered execution. 💸 In the future, the biggest winners won’t be the experimenters running the most tests, they’ll be the ones versed enough to let AI do the testing for them. How do you see AI changing your role as en experimenter? Share below: ⬇️

Agentic AI trends that are a reality already (or someone's working on it 😄): 1. AI Agents won’t just save time — they’ll make money. AI agents will shift from boosting productivity to generating revenue directly. ⏩️ Example: An agent closes outbound deals, writes term sheets, or wins new clients autonomously. 2. Agents will help phase out legacy systems. Instead of replacing old CRMs or ERPs overnight, agents will quietly absorb and replace them from the outside in. ⏩️ Example: An agent learns your workflow, automates key actions, makes the system obsolete over time, and codes them. 3. Agents can mimic human behavior. New AI agents simulate real personalities and groups — unlocking a new kind of behavioral A/B testing. ⏩️ Example: Test how 1,000 investors might react to your pitch deck before ever sending it. Take a look at the research from Stanford University. Link in the comments. 4. Agents will pay each other. Financially autonomous agents can now manage wallets, pay for APIs, or contract other agents. ⏩️ Example: One agent pays another to complete a task, like gathering market data or translating a deck. Project: Payman Ai 5. AI-native fraud is coming fast. Fake voices, documents, and faces will flood markets — especially in finance, identity, and compliance. ⏩️ Example: A deepfaked CEO voice authorizes a $1M transaction. Detection tools must keep up. 6. AI-native institutions are next. AI VCs already exist - AI banks, PE firms, and hedge funds are on the horizon. ⏩️ Example: An AI agent allocates capital, writes IC memos, and reports to LPs without human input. We are building something fascinating here. But also check out one of the Y Combinator startups I left in the comments. 7. New multimodal AI like GPT-4o changes the game. Agents can now see, hear, and speak - making them more useful in real-world tasks. ⏩️ Example: An agent reads a contract PDF, checks for risks, explains it on a call, and sends a summary. 8. AI agents will be insured. As agents make critical decisions, enterprises will insure them like human employees, but we still need to minimize hallucinations. ⏩️ Example: A credit agent makes a false investment call → insurance covers the loss. ARE WE IN THE FUTURE? #AI

🚀 AI Agents: 4 Trends to Watch in 2025🌍💡 AI agents are revolutionizing industries, moving beyond copilots to autonomous digital workers 🤖. As we enter 2025, four key trends are shaping the AI agent landscape: 1️⃣ Big Tech & LLM Developers Dominate General-Purpose Agents 🔹 Tech giants (OpenAI, Anthropic, etc.) are driving AI advancements, making agents cheaper, more powerful, and widely available. 🔹 400M weekly users on ChatGPT showcase the massive distribution advantage. 🔹 Enterprise adoption is increasing, but big tech’s dominance pressures startups to specialize. 2️⃣ Private AI Agent Market Moves Toward Specialization 🔹 Horizontal AI applications (customer support, software development) are crowded – differentiation is key. 🔹 Industry-specific AI agents in healthcare, finance, compliance, and logistics are poised for growth. 🔹 Deeper workflow integrations & leveraging proprietary data will create competitive moats. 3️⃣ AI Agent Infrastructure Stack Crystallizes 🔹 The AI agent ecosystem is evolving into a structured stack with specialized solutions: ✅ Data curation (LlamaIndex, Unstructured) ✅ Web search & tool use (Browserbase) ✅ Evaluation & observability (Langfuse, Coval) ✅ Full-stack AI agent development platforms gaining traction 4️⃣ Enterprises Shift from Experimentation to Implementation 🔹 63% of enterprises place high importance on AI agents in 2025. 🔹 Challenges remain: Reliability & security (47%), Implementation (41%), Talent gaps (35%). 🔹 Solutions: Human-in-the-loop oversight, stronger data infrastructure, and enterprise-grade agent platforms. 🚀 2025 is a breakout year for AI agents – the shift from copilots to autonomous digital workers is happening now! 📈 #AIAgents

🔥 Apple's new AI hub isn't about smart homes - it's about to disrupt your entire B2B sales playbook. Here's what most sales leaders are missing: While everyone's focused on the consumer angle, Apple's entering the enterprise AI space through the back door. Their new command center isn't just controlling thermostats - it's validating the future of AI-powered workspace management. Think bigger: This isn't a gadget launch. It's Apple's first dedicated AI hardware product with an "AI personality." Translation: Your enterprise clients are about to expect this level of AI integration everywhere. As a product strategy consultant, I'm seeing a clear pattern: Companies that integrate AI companions into their B2B solutions are closing deals 3x faster. Apple just made this mainstream. Action steps for B2B sales leaders: • Audit your product's AI integration capabilities this week • Map out AI touchpoints in your current sales process • Schedule demos showcasing AI-powered features I help B2B companies navigate the AI transformation in their sales processes. Want to stay ahead of this curve? Let's talk: https://lnkd.in/eb-twspd What's your take on AI personalities in B2B sales? Drop your thoughts below 👇 #B2BSales #AIStrategy #EnterpriseAI

The PMM leaders who will thrive are already building these AI capabilities. 🔍 Market & Competitive Intelligence Crayon: Automated competitive tracking, real-time alerts & actionable battlecards Brandwatch: AI sentiment analysis & social listening for emerging trends Perplexity AI: Rapid research with cited insights for strategy docs Gong.io: Sales call analysis to identify competitive objections & positioning gaps 👥 Customer & Insights Analysis Otter.ai: AI-powered interview transcription with automated pain point identification UserVoice: Scale feedback analysis & auto-categorize feature requests ChatGPT/Claude: Synthesize research into detailed buyer personas & test messaging Adobe GenStudio (Enterprise): AI persona generation with behavioral traits ✍️ Messaging & Positioning Writer.com: Brand compliance automation across all channels Jasper AI: Generate 20+ headline variations for A/B testing Copy.ai: Rapid email subject lines & ad copy variations 🎨 Content & Asset Creation Adobe Firefly: Generate on-brand images with text prompts in Creative Cloud Adobe Express: AI video content with templates & Dynamic Animation Synthesia: AI explainer videos with virtual presenters in multiple languages 🚀 Go-to-Market Planning & Execution Airtable AI: Campaign trackers with embedded AI risk flagging Notion AI: "Create 90-day launch plans with milestones" automation Adobe Journey Optimizer B2B (Enterprise): AI agents identify key decision makers HubSpot AI: Automate lead qualification & optimize email send times 📊 Performance Measurement & Reporting Adobe Analytics: AI-driven insights on conversion drivers & drop-off points Mixpanel: Behavioral pattern analysis & conversion optimization Amplitude: Auto-surface insights & predict user churn 🤖 Agentic AI & Automation Adobe Agent Orchestrator (Enterprise): Deploy AI agents for website optimization & content production Zapier/Make.com: Autonomous workflows - auto-update battlecards when competitors launch 🎯 Sales Enablement Automation Adobe GenStudio + Zapier: Auto-create sales collateral when products update Notion AI + Gong.io: Turn loss analysis into objection-handling scripts Gamma + HubSpot: Auto-update sales decks with fresh competitive intel ChatGPT/Claude: Generate pricing objection scripts tailored to personas 🗺️ Customer Journey Optimization Adobe Journey Optimizer B2B (Enterprise): AI-powered decisioning for each stakeholder Mixpanel + ChatGPT: Identify content gaps & generate recommendations 📈 Predictive Analytics & Forecasting Adobe Analytics + Tableau: Predict launch success using historical data Amplitude: Forecast performance from early engagement patterns 🔄 Retention & Churn Prevention Adobe Experience Platform (Enterprise): Identify at-risk segments with unified profiles HubSpot AI + Marketo: Auto-generate retention campaigns based on churn risk

I've noticed a race amongst many "AI" B2B SaaS companies recently. And that race is building a Knowledge AI product/feature. (aka a chatbot that can answer any question about your business by ingesting data and context from all your other apps and files) Just to share a few examples - Dropbox recently launched their new product Dash, Notion launched Notion AI, Coda launched Coda Brain in beta, and we have multiple customers building for this exact use case. But why has this suddenly become such a big focus for these companies? Here’s my theory, which breaks down into two parts: 1. I predict that In 5-10 years, every B2B company will be using an AI Knowledge product as their go-to place to ask questions about the business, from sales performance to product questions. This will unlock a huge seat-based expansion opportunity (one seat for every employee) that will be coupled with high daily usage. But this is not the exciting part. 2. By being connected to all of a customer’s other apps and having all of this business context, the AI Knowledge product is perfectly positioned to become a general purpose AI employee/copilot that can take action. “Set up a meeting with sales” —> send an invite via Google Calendar. “Identify areas of improvement for our worst performing sales reps“ —> analyze CRM data and Gong transcripts, and summarize insights. If this is indeed what these companies are building towards, and they’re successful in executing that vision, a large part of the work we currently do manually will be done via APIs all through this AI copilot. That would make these companies THE central operating systems for B2B companies, uprooting all other ‘platform’ companies today. Seems like a future worth betting on. Am I out of my mind or do you think this is the future we’re moving towards in SaaS?

Mary Meeker and the Bond team have released their latest 300+ page annual report, this time all on AI It's very good but long, so I've summarized the Top 10 Points for B2B and enterprise founders here: #1. AI User Adoption Is Literally Unprecedented We know this, but still, the numbers do sort of blow your mind: ➡️ ChatGPT: 0 to 800MM weekly users in 17 months (vs. Netflix’s 10+ years to 100MM) ➡️ Time to 100MM users: ChatGPT (2 months), TikTok (9 months), Instagram (2.5 years) ➡️ Global adoption: 90% of ChatGPT users are outside North America by Year 3 (vs. Internet’s 23 years to reach this level) .Why This Matters for B2B: Unlike previous tech waves that started in Silicon Valley and slowly diffused globally, AI hit the world simultaneously. This means your global TAM expanded overnight, but so did your competition. Every B2B and SaaS company now competes in a global, AI-enabled market from Day 1. The Kicker: ChatGPT’s daily usage increased 202% over 21 months, with users spending more time per session (47% longer) and having more sessions per day (106% more). This isn’t just adoption – it’s addiction-level engagement. #2. The Infrastructure Math Is Unprecedented The Capital Intensity Is Off The Charts: ▶️Big Six tech CapEx: $212B annually (63% YoY growth) ▶️Microsoft AI business: $13B run-rate (175% YoY growth) ▶️NVIDIA data center revenue: $39B quarterly (78% YoY growth) ▶️Amazon AWS CapEx as % of revenue: 49% (vs. 4% during initial cloud buildout) 💡What’s Really Happening: This isn’t just “cloud 2.0” – it’s the biggest infrastructure buildout in tech history. Companies are spending more on AI infrastructure than entire countries’ GDP. xAI built a 200,000 GPU data center in 122 days (faster than building a single house). 👉For B2B and SaaS Leaders: The infrastructure layer is being rebuilt from scratch. If you’re not thinking about how to leverage this massive compute capacity, you’re missing the biggest infrastructure opportunity since the cloud transition. T #3. China Is Playing a Different Game Entirely The Models You’ve Barely Heard Of: ▶️DeepSeek R1: 93% performance of OpenAI’s o3-mini at fraction of training cost ▶️Alibaba Qwen 2.5-Max: Outperforms both DeepSeek and ChatGPT on key benchmarks ▶️Baidu Ernie 4.5: 80% cheaper than predecessor, costs 0.2% of GPT-4.5 Market Reality Check: ✅China leads in open-source AI model releases (3 large-scale models in 2025 vs. US competition) ✅Chinese AI apps dominate domestically: Top 10 AI apps by MAUs in China are all domestic ✅DeepSeek rose from 0% to 21% global LLM user share in just months ✅China has more industrial robots installed than rest of world combined 🌎Geopolitical Stakes: This isn’t just about better chatbots. China views AI supremacy as essential to geopolitical leadership. As Andrew Bosworth (Meta CTO) noted: “This is our space race…there’s very few secrets. And you want to make sure that you’re never behind.” 4-10 at link in comments!