How to Identify Opportunities for AI Innovation

Checklist for you when you want to develop AI use cases in your organisation ✅ If you’re exploring AI in your organisation, the hardest part is often knowing where to start. Here’s a simple checklist to guide you through identifying and shaping AI use cases that actually deliver value: Before jumping into use cases, take one crucial step: Assess your AI maturity first. Run an AI Maturity Assessment to understand your organisation’s current capabilities: strategy, data, tools, skills, and governance. This shows you where you stand — and prevents you from aiming too high or too low. Once you have clarity, move on to shaping specific use cases: 1️⃣ Define the problem clearly Frame the problem in operational terms. Make sure all stakeholders share the same understanding of the issue. 2️⃣ Link it to business impact Ask: If we solve this, what changes? Why do we want to solve this problem? Impact can mean efficiency gains, cost reduction, improved customer experience, reduced risk, or new revenue opportunities. 3️⃣ Data management: sources, access, structuring, cleaning - Sources: Where is the data located? - Access + Silos: Who can retrieve and use it? - Structuring: Is the data in the right format, linked, and standardised? - Cleaning: Remove duplicates, fix errors, and fill gaps to ensure quality. - Ownership: Assign Data Owners and clarify responsibilities Without accessible, high-quality data, no AI use case can deliver real value. 4️⃣ Check feasibility Beyond data, assess process readiness: are workflows digitised and stable enough? Is the AI approach we chose feasible and within our AI governance and security (AI chat, workflow, automation, agent)? 5️⃣ Prioritise quick wins Focus on achievable pilots with visible impact in weeks. Use small-scale success to build trust and demonstrate value. 6️⃣ Engage the right stakeholders Involve process owners, end users, IT, and compliance early on. 7️⃣ Assess risks & compliance Consider data privacy, ethical risks, bias, and regulatory constraints. Address these proactively to avoid showstoppers later. 8️⃣ Plan for scale Think beyond the pilot: can the solution be replicated across teams or geographies Avoid “one-hit” pilots that don’t connect to a bigger roadmap. 9️⃣ Measure success Define KPIs before you start: time saved, cost reduction, error rate, customer satisfaction, revenue growth. Clear evidence makes it easier to secure further investment. Start small, pilot fast, learn, adapt — and then scale what truly delivers business value. Where is your biggest challenge today in developing AI use cases?

The biggest opportunities for AI startups today Noam Segal and I surveyed my readers about how they're using AI today, and more importantly, how they *want* to be using AI. For PMs, the biggest opportunity is research. User research shows the largest demand gap of any task. Only 4.7% say it’s their primary AI use case today, but nearly a third want it to be. PMs have figured out how to use AI for output tasks like writing PRDs and drafting communications, but they’re hungry to apply it upstream, to the messy work of understanding what to build. Prototyping is a breakout category across functions, both today and in the future. For PMs, “creating mockups/prototypes” jumps from 19.8% (currently using) to 44.4% (want to use next), a +24.6pp swing that makes it the single most-wanted future use case. For designers, prototyping and interaction design show similar momentum (+27.8pp). This tracks with the rise of tools like Lovable, v0, Replit, and Figma Make. Engineers are shifting their use of AI to handle work after writing the code. Writing code is by far their most popular use case (51% current), but it has a demand gap of only +5.6pp. However, documentation (+25.8pp), code review (+24.5pp), and writing tests (+23.5pp) all show massive opportunities for growth in engineering AI tooling. Founders are doubling down on AI as a thinking partner. Product ideation shows massive demand, jumping from 19.6% (currently using) to 48.6% (want to use next), a +29.0pp gap. Growth strategy and GTM planning (+24.7pp) and market analysis (+24.0pp) follow close behind. Founders already use AI heavily for personal productivity (32.9% currently), but they want to move upstream. They’re looking for pressure-test ideas, explore markets, and think through go-to-market. AI as a co-founder, not just an assistant. Full report: https://lnkd.in/gR5G88yA

Y Combinator JUST revealed its latest picks for the next wave of startups to fund in 2025 🔥 Key Trends from YC’s New RFS: • AI is moving beyond augmentation to complete automation of roles. • The main opportunity lies in applying AI to specific industries, not simply improving AI itself. • There’s increasing demand for infrastructure and tools to scale AI. • Optimizing systems from the ground up is once again a priority. Most Promising Opportunities: • AI App Store & Supporting Infrastructure: • Create a platform akin to the iOS App Store, but for AI agents. • Prioritize privacy, shared memory, and seamless distribution of these agents. • Vertical AI Agents: • Develop AI that replaces specialized job functions like tax accounting or medical billing. • Aim for full automation of tasks, rather than merely assisting human workers. • AI Developer Tools: • Provide solutions to help developers manage AI agent teams. • Build deployment, testing, and monitoring tools to make AI development more efficient and reliable. Market Math: • 4 million people work in compliance/auditing. • $8,000–$50,000/year is spent on legal templates alone. • Entire professions are now in the process of becoming fully automated. • The best opportunities target high-value, repetitive work. Underexplored Areas: • AI code generation optimized for specialized hardware. • Automating data center operations. • AI-driven document handling systems. • B2A (Business-to-Agent) infrastructure solutions. What Defines a Strong YC AI Startup: • Deep knowledge of a specific industry or vertical. • Commitment to full task automation, not just incremental assistance. • A clear, realistic path to revenue. • Solutions that scale AI infrastructure or development. In short, Y Combinator isn’t looking for better AI technology. They want startups that find smarter, more innovative ways to use existing AI to transform industries.

Small experiment: AI is at a tipping point. After analyzing 20,000+ NEURIPS research papers and tracking 950+ AI startups, we’re seeing clear signals about where innovation-and business opportunity-are headed next. 🔎 Mainstream Trends: Enterprise AI Infrastructure: Despite 2,400+ research papers and a market set to hit $60–82B in 2025, only a fraction of companies have fully adopted enterprise AI. Huge room for growth in deployment automation, LLM optimization, and workflow tools. AI Safety & Governance: Nearly 2,000 papers focus here. As regulations tighten, demand is surging for compliance, bias detection, and privacy-preserving solutions. Generative AI 2.0: With 1,500+ recent papers and a $22B+ market forecast, the future is in industry-specific, controlled, and multi-modal generative AI. 🌱 Fastest-Growing Niches: Neuro-symbolic AI: 600% research growth, high commercial gap-think explainable, reasoning-driven AI. Few-shot & Privacy-Preserving Learning: Rapid research growth but little market presence-prime for new ventures. 📊 Market Gaps = Startup Goldmines Unsupervised, self-supervised, and few-shot learning. 🔮 What’s Next (2025-2027)? Highest Potential: Enterprise AI infrastructure, AI safety/governance, and specialized industry solutions. Strong Potential: Healthcare AI, multimodal systems, edge AI. Emerging: Specialized LLMs, autonomous systems, next-gen generative AI. ⏳ Insight: There’s typically a 1–2 year lag between research peaks and real-world products. Where do you see the biggest opportunity for AI innovation? Are you building in one of these spaces, or have a perspective to share? https://lnkd.in/gy3yVmWM #AI #ArtificialIntelligence #Innovation #Startups #ResearchToMarket #FutureOfAI

One reason AI initiatives stall? Few execs use AI in their own work. In 3 hours, I take leaders from “I don’t know” to a POV (co-developed with AI!) on how AI can support key strategic initiatives. To crack the code on exec adoption we: >> Focus on Strategic Use Cases that Click with Execs << To get experience with high value use of AI, we dive into cases that directly enhance executive decision-making and strategic thinking. This tends to be a major eye-opener—most leaders don't realize AI can elevate their highest-level work. Once executives experience immediate personal value, they better understand how AI can have immediate impact across the organization. >> Reframe Mental Models << Generative AI operates fundamentally differently from anything we've seen before, so we need to identify why and how digital change playbooks must shift to leverage this moment. I go straight to the heart of the silent organizational barriers that prevent productive adoption, and how to navigate a path forward. >> Start with the Business, Not the Tech << We don’t begin with AI—we begin with your business. We anchor the process with the breakthroughs that will drive real impact—and to get there, we go analog with brainstorming, whiteboards, and post-its, working to envision what advancement could look like. What could be possible if cognitive limits were lifted? What long-standing friction could finally be overcome? This surfaces a library of meaningful, business-driven opportunities. Then, using proven filters and frameworks, we zero in on the highest-impact places to start applying AI. >> Use AI to Develop AI Strategy << We then—on the spot—collaborate with AI to develop executive viewpoints on how AI can accelerate those strategic priorities. This is hands-on work with AI tools to co-create a path forward, often culminating in each group sharing a lightning talk (co-developed with AI) with the broader team. This approach fast tracks execs to: 1️⃣ Build readiness: Gain deep understanding of the new landscape of use cases today’s AI offers, and the organizational structures needed to effectively harness it. 2️⃣ Map use cases: Develop a prioritized library of strategic use cases ready for immediate collaboration with technology and data teams. 3️⃣ Accelerate alignment: Establish common language and jump-start cross-functional alignment on tackling high-impact opportunities. 4️⃣ Hands-on understanding: Acquire hands-on experience with AI tools they can immediately apply to their most challenging strategic work. What do my clients say about this approach? That their teams shift from skepticism to enthusiasm—hungry for more, and from uncertainty to clarity about the next steps. It’s a remarkable change, especially in a few hours. ➡️ Want to learn more? Let’s talk. #AIworkshop

#AI and #SDLC - What's changing and what #startups can build . Artificial Intelligence (AI) is fundamentally reshaping the Software Development Lifecycle (SDLC), moving it from a human-intensive craft to an AI-augmented process. What are the groundbreaking opportunities? 1. UI/UX Design: From Manual to Curated Creativity 🎨 Today's design workflows, whether starting from scratch or working within existing systems, are riddled with inefficiencies like manual inspiration gathering and tedious design-to-code handoffs. How AI is changing it: AI models can now generate context-aware mockups from feature briefs and brand guidelines, turning designers into curators who review and customize AI-generated options. For implementation, AI can generate production-grade frontend code, allowing engineers to shift from writing boilerplate to reviewing and refining. Startup Opportunities: • AI Designer Assistant: Think of this as a "junior designer" embedded in an organization. It combines a structured component library with an agentic workflow engine to instantly generate mockups aligned with a brand's design system. This is less about inventing new styles and more about automating execution.      • Frontend Execution Agent: This agentic AI system acts like a junior front-end engineer, transforming finalized Figma designs into clean, semantic production-ready code. • Zero-Code App Builder: For non-technical users like small business owners or HR managers, AI can collapse complex app creation into natural language. Imagine telling an AI, "I want a mobile app where customers can book appointments," and it handles the UI, frontend, backend, data, and deployment. This is about delivering outcomes, not just clean code. 2. System Design: Automating the Blueprint 🏗️ System design is critical, yet often a bottleneck, relying on scarce senior talent and informal tribal knowledge. How AI is changing it: AI can ingest vast architectural designs, trade-offs, and best practices to recommend patterns, surface trade-offs, and auto-generate system diagrams and starter code. Startup Opportunities: • System Design Thinker: An AI copilot that acts as a reasoning assistant, helping engineers explore design options, explain pros and cons, and suggest optimal designs based on benchmarks and historical company decisions. This is fundamentally creative work. • System Design Executor: An agentic solution that automates the translation of high-level designs into diagrams, documentation, boilerplate code, and cloud infrastructure templates. This is largely mechanical execution. 3. Code Writing: From Manual Coding to AI-Guided Assembly ✍️ Developers spend 60-70% of their time on repetitive "grunt work". AI models like GPT-4 can now not only read and write code but also reason about it. How AI is changing it: AI can translate natural language into functional code, explain codebases, suggest fixes, refactor modules, and auto-generate documentation.

6 frameworks to cut through AI noise. Leadership offsites are about choices: '𝘑𝘰𝘰𝘴𝘵, 𝘸𝘦 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘥𝘰 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘈𝘐.' '𝘎𝘳𝘦𝘢𝘵. 𝘉𝘶𝘵 𝘸𝘩𝘢𝘵 𝘸𝘪𝘭𝘭 𝘺𝘰𝘶 𝘥𝘰 𝘧𝘪𝘳𝘴𝘵? 𝘈𝘯𝘥 𝘸𝘩𝘺?' That's the moment we need frameworks - not to complicate things, but to simplify the endless options into clear decisions. The 6 frameworks that proved most effective: 1. Map your AI opportunity landscape The AI Opportunities Radar gives teams a shared language. Is this a back-office efficiency play or a game-changing customer experience? Plot it visually and watch the strategic debates become productive. 2. Balance quick wins with transformation The 'low- and high-hanging fruit' framework. Leadership teams need early momentum (quick wins) AND meaningful transformation (big bets). I usually print use cases and let them map them on these straightforward axes. 3. Where will we create value with AI "We'll be 30% more productive with AI!" Really? How? The AI Value framework forces teams to articulate exactly where and how value will emerge - beyond the vague productivity promises. It also highlights the importance of thinking beyond just productivity. 4. Start with real problems, not shiny toys The classic Value Proposition Canvas grounds everything in reality. What jobs-to-be-done can we actually do with AI, and which pains are we solving for? It's key to think from this lens instead of just getting excited about a new AI tool being launched last month... 5. Time your moves strategically The McKinsey 3 Horizons approach helps sequence your AI journey: what do we optimize now, what do we build next, and what new business models might emerge? Without this, teams might try to do everything at once and achieve nothing. 6. Build the full system, not just the tools The AI Strategy Canvas reminds us that successful AI isn't just about the technology - it's about governance, capabilities, ethics, and organizational change. The companies getting real results aren't just deploying tools; they're rewiring how they work. Leadership teams don't need another AI deck, vendor pitch or new shiny tool that will solve everything ;-) they need a map for making choices that stick. Keeping the reality of actually executing on AI in mind. Are you part of a leadership team stuck in AI paralysis? Let's grab a coffee. Creating momentum and helping you choices is what I do.

Step 3 of 7 for AI Enablement: Identify and Prioritize AI Use Cases See full 7-step breakdown here: https://lnkd.in/g3t7MiZb In setting up AI for success, we’ve covered the foundations: Step 1 defined clear business objectives. Step 2 assessed team readiness, revealing gaps to achieve outcomes. Now for Step 3: Identify and Prioritize AI Use Cases. This step isn’t just about knowing where AI could fit; it’s also about evaluating tools to ensure they meet essential requirements—and testing the top choices with trial runs. First: Explore What AI Tools Are Out There Before diving into specific use cases, it’s important to understand the types of AI tools available that could support your goals. If you’re unsure where to start, here are two valuable resources: • Theresanaiforthat.com – A searchable directory of AI tools across industries. • GTM AI Tools Demo Library – A curated list of go-to-market AI tools from the GTM AI Academy (l^nk in comments). Identify AI Opportunities with the PRIME Framework With a better understanding of AI options, use the PRIME Framework to identify use cases that directly address your most critical business gaps: • Predictive: Can AI help forecast outcomes? • Repetitive: Are there time-consuming, repeated tasks? • Interactive: Could AI enhance customer engagement? • Measurable: Can AI provide useful metrics? • Empowering: Can AI support creativity or productivity? Evaluate Tools with a Checklist Once you’ve outlined use cases, evaluate potential tools to ensure they meet critical requirements before trialing them: • Security & Compliance: Does the tool meet company standards? • Governance: Does it support data governance and accountability? • Cost & ROI: Is it cost-effective based on expected value? • Scalability: Can it grow with your team’s needs? • Integration: Will it fit with your current systems? Evaluate Tools: Make sure selected tools meet security, compliance, and integration needs before trial runs. Pilot Testing Once you’ve prioritized and evaluated, move into a pilot phase. Select top tools to trial with a small pilot team. This phase helps test effectiveness, build internal champions, and refine any processes before rolling out to the larger team in Step 4. Your Checklist for Step 3 1. Explore AI Options: Start with Theresanaiforthat.com and GTM AI Tools Demo Library. 2. Identify Use Cases with PRIME: Target high-impact areas. 3. Evaluate Tools with the Checklist: Confirm tools meet security, compliance, and integration needs. 4. Pilot Test: Trial top tools with a small team to validate effectiveness. By following this approach, you’ll set your team up for measurable, AI-driven success with tools that are tested and proven valuable. Ready to PRIME your AI Enablement? Check out free resources in the GTM AI Academy: • PRIME Use Case Guide • Impact-Feasibility Template • AI Critical Requirements Assessment Up next.. Step 4 of 7 for AI Enablement..

Companies aren't telling you they're replacing jobs with AI. And that's your biggest opportunity right now. While headlines focus on the 41% of companies planning AI-driven workforce reductions, there's a fascinating pattern emerging: Organizations are hiding their AI adoption behind terms like "reorganization" and "optimization." Here's the counterintuitive truth: This corporate silence is your early warning system. Three ways to leverage this moment: 𝟭. **𝗥𝗲𝗮𝗱 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝘁𝗵𝗲 𝗟𝗶𝗻𝗲𝘀** When a company announces "operational efficiency" with healthy profits, that's your signal. They're likely testing AI integration. Study these moves - they're showing you exactly where to position yourself. 𝟮. **𝗧𝗮𝗿𝗴𝗲𝘁 𝘁𝗵𝗲 𝟭𝟬% 𝗚𝗮𝗽** Companies are discovering AI can handle 90% of certain tasks, but that critical 10% still needs human expertise. This is your sweet spot. While others fear replacement, position yourself as the essential human element that makes AI solutions work. 𝟯. **𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗔𝗜-𝗛𝘂𝗺𝗮𝗻 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼** Don't just learn to use AI - learn to fill its gaps. Focus on: - Strategic decision-making - Complex problem-solving - Stakeholder management - AI output quality control The real opportunity isn't in competing with AI - it's in becoming the professional who knows how to make AI truly valuable to an organization. Remember: By the time companies start being transparent about AI adoption, the early advantage will be gone. What signals are you seeing in your industry? #careeralchemy #AI #innovation #careers #creativity

Just walked out of the McLaren Technology Center where leaders from across industries gathered to unpack the disruption AI is bringing to jobs and people. One thing stood out. Organizations have to identify and communicate where the new, higher-value opportunities are because investing in people has never been more critical. I shared a simple framework to help the AI leaders guide their teams through the uncertainty. It has worked for us at Genpact. Look for the natural attributes of people and help them identify the right path. Here are four tracks. - AI Value Architects: Encourage your visionaries, the ones who imagine new workflows, identify AI projects, and connect technical possibility to real business impact. Help them step up and redesign how your organization works. - AI Builders: Support those who thrive on creating and building. These are your engineers, data pros, and integrators. Give them the tools and freedom to construct. - AI Whisperers: Spot the team members who master new technology quickly and use it to amplify their skills. Train and empower these super-users to deliver results that previously needed whole teams. - AI Watchdogs: Listen to the critical thinkers, the ones who question, spot risks, and advocate for responsibility. Elevate them to roles in ethics, risk, and compliance to ensure your AI adoption is thoughtful and safe. In times of change, investing in people and helping them find relevance is our responsibility as leaders. This is how we can turn this generational disruption into an opportunity. #futureofwork #artificialintelligence #disruption #careers