Simone Cicero’s Post

If every company is figuring out an AI strategy in 2023, why GTM and growth is still many SaaS founders' top concern? This is one of my key takeaways when I read OpenView's 2023 SaaS Benchmark report and Battery Ventures' State of the OpenCloud report side by side. If you watch many of the brand new fastest-growing AI-native products, they are almost all consumer-facing: for example, one of the biggest AI native products, Midjourney has 20M users and $300~400M ARR. But it still faces significant risks from both sides - LLMs such as ChatGPT or incumbent players such as Adobe. There haven't been a lot of brand-new B2B AI products, many successful examples are AI enhancements on top of existing players: think about GitHub Copilot, 100M paid users, over $100M ARR. Jasper might be considered an example of an AI native B2B product, but because it doesn't occupy a workflow, it is more like a point solution that faces similar risks as Midjourney does. So for many SaaS founders, the first priority is still penetration, market share, and occupying that key workflow within more companies. While AI provides a potential opportunity and wedge to achieve that, it is easier to add AI enhancement on top of an adopted workflow and system of records, vs. the other way around, at least in the near term. Eventually, will there be completely AI-native B2B applications that re-invent the entire workflow? I would imagine so but we need to see bigger changes in how we work first. What do you think? What's your perspective on this? Link to both reports in comments.

In this blog post I worked with ChatGPT, which much of my content is created and checked to ensure relevance, to write about creative destruction. A process that occurs when new technology or processes are introduced to the marketplace to enhance efficiencies. Check it out: 👇 DigiTools Consulting, LLC #AI #Salesforce #CRM #Customer360 #CreativeDestruction

Zeya Yang provides insight in moving from using AI to generate content to using it to automate workflows. One example shows how Claygent can automate competitive analysis of companies, tools or products.

B2B AI applications are more focused than ever on owning the workflow. Last year, we wrote about a transition to “Wave 2.” Wave 1 of B2B generative AI applications was about creating new content — like emails, lists, or marketing copy. Wave 2 focuses on condensing that information to show us *less* content and actually save users time i.e. SynthAI. We’re seeing Wave 2 play out with startups more deliberately targeting workflows by automating processes and highlighting insights. I wrote a follow up piece to share thoughts around how AI and synthesis can help companies own workflows, dive into examples of Figma, Macro, and Clay doing so, and discuss how this trend will continue. Over time, we expect B2B AI applications to become increasingly proactive – possibly reinventing entire user experiences as we know them today. Check it out here! https://lnkd.in/ejwaiHhu

B2B AI applications are now focusing on workflow ownership. Last year marked a shift from content creation (emails, lists, marketing copy) to condensing information and saving time. Startup like TROM.AI™ is increasingly targeting workflows by automating processes and highlighting insights. Over time, B2B AI is expected to become more proactive, potentially reinventing user experiences. #AI #GenAI #ArtificialIntelligence #Reinvent #Growth #Innovation #Futureofwork #IndustrialRevolution4 #AutonomousAIAgent

AI is still all the 🐝 in #B2BSaaS 🔥 🌶️ ♨️ and we don’t see that changing anytime soon. Companies are finding ways to integrate AI into their platforms and have quickly pivoted. 🔛 🤔The downside to this quick pivot is that sometimes websites reflect the rush. 😱 The perfect homepage is a dynamic webpage that constantly evolves – adapting to the characteristics of a company, including its stage, size, product status, market reach, and the ever-evolving industry landscape. We asked our CEO, Elad Hefetz, to tap into his experience building hundreds of websites and provide an assessment. Check out how AI companies like Gong Notion Glean Cresta VEED.IO Sourcegraph and Yokoy scored across five website dimensions 👉https://bit.ly/3t0pKDU #generativeAI #b2bmarketing #websitebestpractices

Owning the Workflow in B2B AI Apps: having reliable, proactive AI capabilities should change how we interact with products at a more fundamental level. https://lnkd.in/guW_6mCK

Great coverage on Workflows in AI Apps, Synthesis AI, and driving towards insights. Especially relatable if you’re already tired of the prompting–reading–re-prompting workflows – some of my takeaways: 🚀 Embracing the Workflow shifts in B2B AI: In the evolving landscape of AI applications, the focus is shifting from mere content generation to owning workflows. Eventually to execute workflows more proactively, reimagining user experiences. 🧠 SynthAI: Synthesis AI focuses on insights for users, condensing information to save time and streamline workflows. This shift is crucial for companies aiming to secure a competitive edge by embedding AI deeply into daily routines and business ecosystems. 🤖 The Challenge of Chat UX: While chat interfaces have popularized LLMs, they can disrupt native workflows in B2B settings. A key is to integrate AI’s input->output capabilities seamlessly into products without adding extra layers of interaction to reduce friction in workflows. 💡 The Click is King — Owning the Workflow: Turning complex processes into one-click solutions that feel integrated and embedded into the user’s environment, with effective governance and HITL oversight, will likely continue to grow and evolve. #B2BAI #GenAI #SynthAI #Tech #AI #a16z Andreessen Horowitz https://lnkd.in/gJqTnf3K

B2B SaaS companies everywhere are embracing new and emerging customer value opportunities brought about by artificial intelligence and machine learning (AI/ML) particularly as a result of awareness and accessibility brought about by generative AI. AI/ML isn’t exactly new, but as it enters the mainstream, SaaS product managers wonder what this means for embedded analytics…do they work separately or in harmony? Embedded Analytics has traditionally been about navigational journeys that lead the user to root-cause and explain why something has occurred…Answering Questions. AI/ML makes it possible for SaaS solutions to determine, without human intervention, actions that might be carried out automatically…Automating Answers. If the SaaS solution answers every question automatically, why do I need analytics?  2 primary reasons: Firstly, not every answer has been automated and it’s likely an open debate whether that will be the case for the foreseeable future.  Should we automate democracy for instance? Secondly, if every answer had been automated, wouldn’t we want some kind of oversight as well as help in fine tuning AI/ML and adjacent models for optimal efficiency? AI/ML and embedded analytics are partners-in-value for your B2B SaaS solution.  Innovative product and development teams think about them together, not as if they are mutually exclusive. #b2bembeddedanalytics #b2bSaaS #SaaSProductManagement #SaaSAnalytics

Current chatbots have a last-mile problem. They read and regurgitate existing knowledge but can’t take action on dynamic data. If a customer of an e-commerce or SaaS tool comes with a query about a feature not working. AI should be able to fetch the user’s data and solve the problem without involving a human customer service rep. Some companies have to have both- which negates the whole point of subscribing to a chatbot. Now some complex feature bugs might not be completely solvable by AI but for a start, it can single-handedly execute the famous refund policy most SaaS tools have. If you have a no-questions-asked 7 days refund policy and a customer comes within 7 days to get his refund, an AI capable of handling dynamic data should: - fetch his email - check if the user’s subscription exists and the customer ID - check if the request is actually within 7 days - and initiate a refund Without a human customer service rep.