DeepSeek AI Business Insights

It's time to take AI seriously The launch of the "Stargate" initiative in the United States, a huge project with a funding range of $100 billion to $500 billion, should be a wake-up call for every business owner, managing director and entrepreneur. This massive investment to build advanced data centres and associated infrastructure across the US (and soon across the world) highlights one undeniable truth: AI is not just the future, it’s now the norm in today’s society. As a business leader, it is crucial to assess how this rapid adoption of AI-driven technologies might impact your market. Whether it’s customer demand, product development, or even recruitment strategies, AI will transform how businesses operate. I believe this is one of those pivotal moments in history where "adapt or die" becomes more than a cliché, it is the reality we face. I have been surprised by how little AI is discussed in business circles in the UK in comparison to what I hear from my colleagues in the USA. To get us thinking let’s explore how AI can directly impact two common types of small businesses: The Small Restaurant: AI tools can revolutionize operations by streamlining tasks like inventory management and quality control. Imagine being able to predict exactly what ingredients you’ll need based on seasonal trends or customer preferences, reducing waste and maximizing profit margins. Additionally, AI can enable hyper-personalized customer experiences, such as tailored menu recommendations or loyalty programs and automate processes like order management and receipt generation. The Independent Consultant: For the independent consultant, you will need to enhance your technology knowledge to ensure you can utilise the wealth of new tools that are coming to market to better help your clients because if not they will move to utilise a "Robot AI Consultant" themselves. In this era of rapid change, success will depend on three core principles: curiosity, continuous learning, and flexibility. 𝐒𝐭𝐚𝐲 𝐂𝐮𝐫𝐢𝐨𝐮𝐬: Keep exploring how AI tools can benefit your business. 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Take the time to educate yourself and your team on the latest technologies. 𝐁𝐞 𝐅𝐥𝐞𝐱𝐢𝐛𝐥𝐞: Be ready to pivot your business model to stay ahead of new competitors eager to disrupt your industry. Building partnerships and rethinking operations will be essential to thrive in this new landscape. Change can be intimidating, but it also brings new opportunities. The businesses that act now to adopt AI will be the ones leading their industries in the years to come. Focus on the importance of maximising your time on what AI can't do, eg, human-to-human interaction, building trust, collaboration... It’s time to prepare for the future. Start small, stay open-minded, and take that first step toward integrating AI into your business operations now. Remember you are not alone, we are all on the same journey so reach out to others and openly share experience.

The Autonomous Business: Get AI-Ready for Decision Intelligence Businesses are transitioning towards autonomy, propelled by advancements in AI and adoption of decision intelligence (DI) practices and platforms have a large part to play in this scenario. This shift necessitates a blend of technology and strategic foresight - the enhancement of human decision-making with AI. By integrating/embedding decision intelligence into business processes, organizations can start building in the autonomous business landscape, ensuring competitiveness and fostering innovation in a future, programmable economy. Key Findings: 🔵 Decision intelligence enhances strategic outcomes by bridging analytics with operational actions. 🔵 Autonomous businesses leverage self-learning systems for strategic growth, reducing manual oversight. 🔵 Decision augmentation, combining human insight with AI, offers greater opportunities than automation alone. Recommendations: 🔵 Develop a robust decision-centric set of models and apply the appropriate composite AI techniques to support autonomous operations. #AIReady 🔵 Promote a culture the explicitly models and uses data for learning and agility to adapt decision processes for optimal outcomes. 🔵 Ensure ethical AI use and transparency to build trust in autonomous business models and decisions. Definitions: #DecisionIntelligence (DI) is a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback. #AutonomousBusiness, Gartner defines as: “a style of business partly governed and majority-operated by self-learning software agents that provides smart products and services to machine-customer-prevalent markets operating in a programmable economy.” I think Decision Intelligence practices and #DecisionIntelligencePlatforms are a major enabler of autonomous business. I am delighted to be publishing some research with my colleague, the amazing Frank Buytendijk, on exactly this topic, shortly. In the mean time, let me know your thoughts, on DI for Autonomous Business.

The role of product management, especially for AI-based products, is changing a lot. Interestingly, a significant number of products are becoming "AI-based" products. You'll often see requests for a stronger technical background alongside traditional PM skills. It's not enough to just know the market and users anymore; product managers now need to understand things like algorithms, data pipelines, and machine learning. This isn't a small change; it's a real shift in what's required. It’s not about knowledge of a toll but the technology. I'm seeing this trend firsthand. Look at product manager job descriptions, and "understanding or working knowledge of AI" is becoming standard. We're also seeing more data scientists and AI engineers moving into product management. This isn't just a career switch; it's a sign that technical knowledge is crucial for building good AI products. For people without this background, it's a big challenge, requiring a lot of learning and a willingness to try new things. Being able to explain complex technical ideas in a way that users understand is now a must-have skill. The key to AI product management is balancing big ideas with what's actually possible. Without understanding AI's strengths and limitations, product managers can easily get swayed by marketing hype or struggle to create realistic roadmaps. It's the difference between a dream and a practical vision. Equally important is building strong communication with engineering teams, not just for technical alignment but for building trust. Don't believe the idea that you don't need technical skills in PM. This trend is only going to get stronger. It's better to adapt and learn than to struggle later. #ExperienceFromTheField #WrittenByHuman

For businesses adopting AI, the promise of savings through role automation can mean the human cost is overlooked.    I’ve noticed plenty of optimism about AI freeing humans from repetitive jobs, but a deeper concern is also emerging: if businesses continue to rely on traditional career paths and capability frameworks, human workers may find themselves sidelined in the future of work.   Whilst the perceived benefits of AI freeing up human time for more strategic roles is attractive, the consequent rising unemployment and lack of training for young professionals is alarming. How can our people effectively make these strategic decisions in the future, if they miss out on the fundamental learning and development offered by entry-level roles?   For long-term commercial success, it is clear businesses have a responsibility to take care of their people and support employees to adapt to the future of work with AI.    🌱Grow your people by investing in reskilling programmes so your team is properly equipped with competitive capabilities to harness AI. 👥 Foster an ethos of human-AI collaboration by amplifying and nurturing the qualities that make us unique – creativity, intuition, compassion and imagination to optimise the benefits of AI augmentation rather than AI replacement. 🤝 Build trust by committing to mitigating any detrimental effects of the technology on people and society and providing transparency and safeguarding around the development and deployment of AI.    Now is the time for business leaders to consider how you are helping workers adapt in the new AI world. And for workers, is your business doing enough?

Everyone says the future of product management is AI-native. But what the hell does it mean to be an AI-native PM? After watching our instructors teach thousands of students at Maven and observing my own team's transformation, I think it comes down to two layers. 1. The technical layer If you want to build AI-first products, you need to know how they work.   • AI fundamentals. What an LLM actually is, the trade-offs of using something like RAG, when to use agents (one or multiple), and what evals are. You need to speak the language fluently enough to collaborate with engineers without a translator.   • Model intuition and selection. When to fine-tune, how cost and intelligence scales with model size.   • AI product sense. AI products have fundamentally different requirements. A mediocre AI experience is worse than no AI experience at all. You need to understand guardrails, failure modes, and how to design for non-determinism. 2. The productivity layer PMs should use AI as a second nature part of their day-to-day work. For existing PMs, this requires shifting their workflows entirely...   • Prototyping. Instead of PRDs, start by using tools like Cursor or Claude Code to ship and iterate on prototypes and feature demos.   • Research and insights. Use LLMs to synthesize data of all types (not just CSVs) into usable insights. Read the original data to ensure accuracy and deeply understand the context the LLM is presenting.   • Strategy and writing. You still do your own thinking, while leveraging AI to fill in the gaps. AI can produce excellent docs and decompose them into tasks given enough context and prompting, but it shouldn't make the final decisions.   • Personal software. Use tools like Claude to build small apps and tools that only you use, optimized entirely for your specific workflows and use cases. Taste and judgement still matter the same as they did before. PMs are still expected to be the CEO of their products. But they also need to be natively using AI in their work, and deeply understand the opportunities to build AI-driven products. P.S. BTW we’re partnering with Lenny Rachitsky to launch a new series of free lessons called “The AI-Native Product Manager”. Check it out: https://bit.ly/4s0mYYj   • The CTO of MySpace turned ML Product Lead at Google, Dmitry Shapiro, on how to best use Clawdbot as a PM   • The 1st Product Manager, v0 at Vercel, Ary Khandelwal, on how PMs can build and *deploy* code with no handoff   • Ex-Head of UXR, Spotify Business, Caitlin Sullivan, on when and how to construct synthetic data for product discovery   • The former CPO of LinkedIn, Tomer Cohen, on becoming a full stack builder with AI   • Former Director of Growth at Gitlab, Hila Qu 曲卉, on the The AI-powered VP of Growth playbook   • Former FDE Lead at Palantir and Citadel, Vinoo Ganesh, on building products like a forward deployed engineer   • Product Lead at Roblox, Peter Yang, on AI Powered Product Skills for Executive Leaders & GMs

This report from ICONIQ focuses on the "“how-to”: what it takes to conceive, deliver, and scale AI-powered offerings end-to-end", mapping out how what startups building AI products are doing - successfully or otherwise - based on a survey of 300 startup executives. They define "AI-native" as where the core product or business model is AI-driven, while "AI-enabled" adds AI capabilities to existing products or new non-core AI products. There is a lot in the deck, worth going through, but a few highlights: 🚀 AI-native companies scale faster and earlier. 47% of AI-native products are already in the scaling phase versus only 13% of AI-enabled products, showing structural advantages in speed and maturity. 💡 High-growth companies build more agentic workflows. 47% of high-growth firms are actively deploying AI agents in production, compared to 32% of other companies. 🧠 External AI is optimized for accuracy, internal AI for cost. Accuracy is the top model selection factor for customer-facing products (74%), while cost leads for internal tools (72%). 🧾 API costs are the biggest budgeting challenge. 70% of respondents rank API usage fees as the hardest infrastructure cost to manage, outpacing inference, training, and storage. 💰 Inference spending explodes post-launch. High-growth companies spend up to $2.3M/month on inference at scale—more than 2x that of their peers. 📊 Coding tools lead in real productivity gains. 65% ranked AI coding assistants as the top driver of productivity, with high-growth companies reporting 33% of code written by AI. 📈 AI engineering headcount is rapidly increasing. High-growth companies expect 37% of engineering roles to focus on AI in 2026, up from 28% in 2025. 🧩 Open-source and inference optimization are key cost controls. 41% are switching to open-source models and 37% are optimizing inference efficiency to combat spiraling costs. 🏷️ AI pricing is still immature and mostly bundled. 73% of AI-enabled companies either include AI in premium tiers or at no extra cost, but 37% plan to revise pricing based on usage or ROI. ⚖️ Explainability is a critical barrier to trust. 42% of companies cite explainability as a top-3 deployment challenge, especially in regulated sectors. 📉 Only half of employees use AI tools regularly. Despite 70% of employees having access to AI tools, only 50% use them consistently—dropping to 44% in $1B+ enterprises. 🧪 Monitoring is common but automation lags. 75% of scaled AI products include advanced monitoring, but few teams have fully automated retraining pipelines. 🛠️ Proprietary models are a high-growth differentiator. 54% of high-growth firms fine-tune foundation models and 32% build proprietary models, compared to 32% and 20% respectively among others. 📦 AI-native firms build more agentic and vertical tools. 79% of AI-native firms focus on agentic workflows, while 56% also build vertical applications tailored to specific industries.

Are AI Native companies today showing us the future of the org chart? If you want to know what org charts will start to look like in a few years, consider looking at what the top AI Native companies look like today. —————————— We analyzed functional distributions across 1M+ employees in Pave's real-time dataset, comparing the top AI-native companies (70% of the Forbes AI 50 participate in Pave's data including leaders like OpenAI) against the broader non-AI tech market. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗔𝗜 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗼𝗿𝗴 𝗰𝗵𝗮𝗿𝘁 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲: 1️⃣ 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗱𝗼𝗺𝗶𝗻𝗮𝘁𝗲𝘀. ~50% of headcount at top AI companies sits in engineering vs. ~37% at non-AI tech. 2️⃣ 𝗦𝗮𝗹𝗲𝘀 𝗵𝗼𝗹𝗱𝘀 𝘀𝘁𝗲𝗮𝗱𝘆. ~19% at AI companies vs. ~20% at non-AI tech. Even the most AI-native companies are investing in humans to sell their offerings. The product may be AI, but the GTM motion is still involving humans. 3️⃣ 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗴𝗲𝘁𝘀 𝗰𝘂𝘁 𝗶𝗻 𝗵𝗮𝗹𝗳. ~3% at AI Native companies vs. ~7% at non-AI tech. 4️⃣ 𝗙𝗶𝗻𝗮𝗻𝗰𝗲, 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗹𝗹 𝗿𝘂𝗻 𝗹𝗲𝗮𝗻𝗲𝗿. Each of these functions runs 1-2 percentage points thinner at AI companies. Not dramatic individually, but collectively it paints a picture: AI-Native companies are concentrating headcount in builders and sellers and generally compressing everywhere else. —————————— 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝗿 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: We recently showed that the entry-level IC workforce is shrinking across all of tech, and that AI-Native companies had ~13% more senior ICs and ~16% fewer junior employees: https://lnkd.in/gYHfxv2Y Today's data adds another dimension. It's not just the level mix that looks different at AI companies. It's the entire functional shape of the org. More engineers. Similar sales investment. But fewer support staff. And a leaner back-office.

If you're a product manager and not using generative AI yet… you're falling behind. Over the past few months, I’ve been exploring how PMs across industries are adopting AI - not for hype, but to actually get things done faster and smarter. I got inputs from 300+ product managers and here’s what real product managers are using generative AI for: ↳ Summarizing customer feedback from surveys and reviews ↳ Writing better PRDs, FAQs, and user stories (yes, even from Figma screens!) ↳ Brainstorming product ideas and outlining go-to-market strategies ↳ Automating SQL queries and documentation ↳ Creating wireframes, mockups, and prototypes in minutes ↳ Preparing pitch decks, emails, and product update announcements ↳ Synthesizing competitor analysis and market research ↳ Managing team workflows and Slack/Notion chaos with AI agents From ChatGPT and Claude to Notion AI, Cursor, and Replit - PMs are building powerful workflows around AI. Some have even built their own agents for writing specs or organizing roadmap inputs. The goal? Free up time for deep thinking and high-impact decisions. This isn't about replacing PMs. It's about amplifying what we do best: understanding users, aligning teams, and shipping value. If you’re just starting out, begin small: ↳ Ask AI to rewrite an email ↳ Summarize a user interview ↳ Draft a product update from bullet points You’ll be surprised how quickly it becomes your second brain. Are you already using AI in your product workflow? Follow Lokesh Gupta for more such insights.

While Palantir, OpenAI, and Anthropic generate headlines with their exponential ARR growth, most private companies at the intersection of SaaS and AI struggle to optimize their Go-to-Market strategy. Perfecting GTM is particularly vital for companies beyond the 180 unicorns—those aiming to reach the $100M ARR milestone. Here's an insightful report on the current GTM landscape, especially relevant as vertical SaaS companies increasingly shift toward AI and AI startups pivot from consumer to enterprise markets. Key takeaways from ICONIQ: 🔶 AI-Native vs. Traditional SaaS: Performance Gaps Widening 🔸 AI-Native Outperformance: AI-Native companies significantly outperform peers in conversion rates, especially in the free trial/POC stage. Faster ROI and clearer value help close deals despite market headwinds. 🔸 Team Structure Evolution: AI-Natives allocate more headcount to Post-Sales teams (e.g., forward-deployed engineers supporting customer onboarding/adoption), optimizing for long-term customer value. Non-AI firms are embedding CS functions throughout the GTM org, moving away from standalone CSM teams. 🔶 GTM Motions: Multi-Channel, Hybrid, and Partnership-Driven 🔸 Hybrid Motions Rising: There is a pronounced shift toward blended top-down and bottom-up customer acquisition, reflecting the need to engage multiple stakeholders. 🔸 Partnerships as Key Levers: Investing early in partner ecosystems pays off as companies scale: >80% of $25M+ ARR companies derive at least 10% of revenue from channel sales. 🔶 Internal AI Adoption: Foundation for Lean, High-Performance Go-To-Market 🔸 AI as a Team Multiplier: Founders who invest in embedding AI into GTM operations (especially in Marketing, SDR/BDR, and AE teams) see marked productivity and efficiency gains. 🔸 Core Use Cases: Lead generation (61%), content/campaign creation (58%), and meeting transcription/analysis (71%) are the most common entry points for GTM AI—start there if you haven't already. 🔶 Key recommendations for founders and growth teams: 🔸 Benchmark AI Maturity: Honestly assess where your GTM org stands on AI adoption. Prioritize embedding AI in lead gen, content, and sales workflow automation. 🔸 Invest in Technical Post-Sales: As products become more AI-powered and complex, ensure support and onboarding teams are staffed with technically adept talent who can drive value and adoption. 🔸 Double Down on Partnerships: Build out your channel strategy early, even modest revenue from partners signals scalability and can de-risk revenue concentration. 🔸 Innovate on Pricing: Consider hybrid models if appropriate for your product, especially for AI solutions. 🔸 Track the Right Metrics: Focus not just on lagging indicators like ARR and NRR, but also top/mid-funnel conversion, pipeline coverage, and leading indicators of GTM health (AI adoption, team efficiency, and partnership contribution). #gotomarket #GTM

AI won't replace product managers. It will make the bar much higher. Last week's Product Thinking podcast brought together some incredible voices like Jessica Hall (CPO at Just Eat Takeaway.com), Mario Rodriguez (CPO at GitHub), Steve Wilson (CPO at Exabeam), and Darren Wilson (CPO at Soul Machines), among others. But one insight from Anthony Maggio (VP Product Management at Airtable) got me thinking about the future of Product Management. "AI is actually going to increase the expectations of the PM function. There's a lot of things that PMs do that AI is actually already quite good at. Taking data and analysis from many different sources and using that to craft strategy and set goals or write PRDs." Here's the paradox: as AI handles the tactical work, expectations for strategic thinking will skyrocket. Right now, many PMs spend hours collecting data, writing PRDs, and synthesizing basic market research. That's time not spent on deep customer insights, competitive intelligence, or identifying emerging opportunities. When AI can pull together market data and draft documents in minutes, what separates good PMs from great ones? The ability to read between the lines, spot patterns others miss, and make strategic bets that aren't obvious from the data alone. As Anthony put it, "historically there have been so many inputs that it is difficult for PMs to stay on top of all of those things proactively." AI removes that excuse. The PMs who thrive will use this shift to become true strategic thinkers, not just feature managers. How are you preparing for higher expectations in your PM role?