Small AI Projects That Drive Transformation

A housing nonprofit training a 10MB model that predicts eviction risk better than any generalist AI. A food rescue org building a routing algorithm that runs on a phone and reduces waste by 40%. A youth services agency creating a risk assessment tool that fits on a thumb drive. These models work because they're trained on specific, relevant data. They don't need to know Shakespeare to predict housing instability. They don't need to write poetry to optimize food distribution. Google.org's latest research shows that specialized small models outperform large language models by 3x on domain-specific tasks while using 1/1000th the computing power (better for the environment too). This matters because small models can run locally, preserving privacy. They can be trained by nonprofits themselves, ensuring relevance. They can be deployed on existing hardware, eliminating infrastructure barriers. The future of AI in social impact isn't one massive model to rule them all. It's thousands of small, focused models solving specific problems brilliantly. Stop waiting for AGI. Start building narrow AI that actually works. Worth a read here: Distilling step-by-step: Outperforming larger language models with less training data and smaller model sizes:  https://lnkd.in/gBVQ4gWq #SmallModels #LocalAI #PracticalAI #NonprofitInnovation #TechStrategy

MIT: “95% of AI projects fail”. Wharton: “74% see ROI with AI.” Both stats are true. And both completely miss the real point. MIT studied big AI transformation projects: the huge, top-down initiatives where companies try to rebuild everything at once. Wharton studied small workflow-level AI: teams using Chat-GPT, Claude, and Copilot inside processes that already work. Same tech. Different conditions. Opposite outcomes. And here’s the uncomfortable truth that neither study says out loud: AI doesn’t magically create ROI. But Fixing workflows does. And AI amplifies it. This is why senior executives report strong ROI… while the middle managers are saying it’s too early to tell. Leadership sees the strategy. But operators live inside the broken processes. No workflow change = no AI win. It really is that simple. And you see this play out in the wild every day. Some companies want “AI transformation.” Others want “AI employees.” But the ones actually getting results? They’re fixing one workflow at a time and stacking the wins. Just look at the Forms AI employee we built last week: - No massive project - No digital transformation - No new hires - No layoffs - Just one workflow redesigned around what AI is good at It now handles the "inbox hell" for our client. It classifies, responds, routs, and logs... and it’s saving their people hundreds of hours per year. That’s just one win! Now imagine stacking 10 of these... How about 30? That’s the real ROI everyone keeps arguing about. So yes...MIT is right. Big AI fails. Wharton is also right. Small AI succeeds. But the real story is this: Companies don’t fail with AI because the tech is bad. They fail because the workflow and the execution are bad. Start small. Fix one workflow. Then let AI scale what already works. That’s where the compounding ROI lives. #AIstrategy #workflowdesign #AIautomation #futureofwork #Braive

Sharing two transformative projects I've been working on for pharma clients in Boston. Project 1: AI-Powered Regulatory Compliance (Completed) Regulatory analysts typically spend hours daily reviewing 500+ page manufacturing documents, cross-referencing them against drug regulations across different geographies, a process that can take weeks and delay commercialization. We built an AI tool that automates this entire workflow: 1. Upload manufacturing documentation and the system analyzes it against relevant regulations 2. Identifies compliant and non-compliant elements with specific justifications 3. Enables analysts to dive deeper through conversational AI for nuanced decisions Impact: Weeks of work reduced to minutes, accelerating time-to-market while maintaining regulatory rigor Project 2: Strategic Intelligence Platform (Coming Soon) Now we're building an AI-powered platform that transforms how pharma companies identify and pursue drug development opportunities: 1. IP & Competition Auto-Capture – Real-time tracking of IP filings, competition, and local manufacturers 2. Molecule Opportunity Finder – Discover high-potential molecules using TAM, global IP status, and local risk analysis 3. Development Phase Navigator – Track which companies are working on target molecules across all clinical phases 4. Clinical Insights Engine – Monitor trial results and compare novel drugs with existing treatments 5. Regional Risk Watch – Smart alerts when competitors file IP or receive approval in key markets 6. AI Development Recommendations – Strategic suggestions based on market opportunity, IP landscape, and unmet needs

#PodcastAlert! Today, I sat down with Olga. Topchaya, founder & CEO of Lapis AI Consults – one of those grounded, no‑nonsense voices on practical #AI for #smallbusinesses and #startups. We badly need these voices to ensure enterprises benefit from AI without overspending on initiatives that don't solve real-world problems and are based on fantasies around use cases. My audience knows how much I hate these! AI makes an impact only if the problem is defined, the context is clearly understood, and the workforce is trained (and understands the 'why' behind an effort). Olga's work and her guest blog for my #AIEdutAInment website focus on a simple idea: 👉 AI should start with business problems, not models. She helps founders and #SME leaders identify where people spend time on repetitive, low‑value tasks, then designs lightweight #AIworkflows that actually ship – from customer support copilots to internal assistants that prep research, summarize meetings, and keep #CRMdata clean. In our conversation, we covered: How a small team can get real value from AI without huge budgets or in‑house #datascientists. Why "start with one painful workflow" beats grand #AItransformation roadmaps. Concrete examples of SMEs cutting support costs and freeing 20–30% of team time through #targetedautomation. The human side: training people, setting expectations, and building AI products that teams actually want to use, not bypass. If you're a founder, SME leader, or operator who is AI‑curious but budget‑constrained, this episode is for you. AI is no longer just for big guys – it's increasingly a small‑business advantage when implemented thoughtfully. This podcast builds on episode 42, "The Lottery of AI Impact," featuring Silvio Gerlach. Silvio is an economist and educator working to improve the situation of struggling AI implementations in Germany. In my "Leading Through Disruption" leadership series, I blogged about one successful effort to introduce AI and automation into a European family-owned OEM - check it out to learn more about the reality of these projects! Please DM me on Instagram @romyandroby and comment here with the topics you would like to see in future "AI Snacks." I am happy about how the podcast evolved. An additional 30 episodes are already planned - great guests are scheduled through September 2026! Stay tuned - it is all about democratizing knowledge and understanding of AI, robotics, and quantum tech with business people (and their families!) The AI Edutainment community now counts 30 tsd people across the globe - and I am immensely grateful to everyone who gave me guidance, asked questions, and encouraged me to keep my head down and simply work on AI Literacy. The ExCo Group TheSocialArchitects Hans-Christian Boos Anthony Scriffignano, Ph.D. Andrea Olsen Lorenz Beyeler Lauren Winnenberg Donnetta Campbell Donnacha Daly Local AI Community (LAC) https://lnkd.in/daQtPXkx

Nobody wants to hear about the CEO who automated meeting notes. They want the story about the five million dollar AI transformation. The enterprise-wide rollout. The impressive dashboard. I've worked with 30-plus companies on AI over the past year. The ones that succeed with big projects always started with boring automation first. A manufacturing CFO I'm working with spent three months automating one thing. Turning PDF distributor reports into spreadsheets. Saved her team 12 hours per quarter. That’s not trivial. Her team saw automation solve a real problem they actually had; one that annoyed them every single quarter. Six months later she proposed a larger AI initiative. Adoption hit 67% in the first month. They had already learned to trust that automation solved their problems, not created new ones. Contrast that with a logistics company I worked with last year. They launched with an impressive AI platform. Beautiful interface. Millions invested. No small wins first. Four months later adoption was at 11%. The teams didn't trust it because they'd never seen the company successfully automate anything that mattered to them before. This pattern keeps showing up. Leaders skip the boring stuff because it's not impressive enough to show the board. They go straight to the transformative AI project. Then wonder why teams ignore it. Teams that automate invoice reminders, support ticket routing, meeting transcription, data entry aren't wasting time on small stuff. They're building organizational muscle memory for trusting automation. That trust is what makes the bigger projects work. Your team will trust AI with the important stuff after you prove it can handle the boring stuff first. #AIAdoption #ChangeManagement #AITransformation #OrganizationalLearning #TrustInTech #EnterpriseAI #LeadershipStrategy #DigitalTransformation

Want your AI projects to deliver real profit? Focus on these two principles: 1️⃣ Sequencing (for big cases) - Break massive projects into smaller chunks - Ensure each chunk delivers value - Make each step unlock the next Until one day you realize: You've actually transformed something. 2️⃣ Orchestration (for small cases) - Connect your Atomic Use Cases - Make them work together - Share data, infrastructure, learnings Small wins compound into bigger impact. Quick example: "AI Email Reply" ❌ The typical approach: "Let's roll out Copilot so people write emails faster" Result: Another high-level experiment nobody remembers ✅ The orchestrated way: 1. Start with simple email classification 2. AI-augment responses for specific classes 3. Generate 95% drafts for proven cases 4. Full automation where it makes sense Result: First step toward real customer service automation. Getting these two principles right lets you implement Profit Milestones as you go. That's why AI success isn't about the technology - it's about the way you put it on a Roadmap.

If you’re an aspiring AI engineer (or already one!) and looking for portfolio projects that are weekend-friendly but still advanced enough to impress, here’s a list of 10 projects you can knock out in 1–2 days. These aren’t toy examples, they’ll expose you to real toolkits (like Fireworks AI, LangChain, AutoGen, LlamaIndex,Mem0, etc.) that people in the industry are actually using. It's a great way to get hands-on experience and build projects that stand out 👇 1️⃣ RAG-Powered Research Assistant 2️⃣ Multi-Agent Workflow Orchestrator 3️⃣ IDE-Native AI Coding Copilot 4️⃣ Custom Evaluation Harness for LLMs 5️⃣ Personalized AI Coach 6️⃣ Video-to-Text Knowledge Explorer 7️⃣ AI-Native Data Pipeline Debugger 8️⃣ Slide Generator from Research Papers 9️⃣ AI-Driven Product Image Analyzer 🔟 Agent-to-Agent Negotiation Simulator Each of these will give you real-world exposure to the problems AI teams are solving right now—and the stack they’re solving them with. Happy Friday ❤️ 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Harnessing the Power of AI: Small Wins Lead to Big Success In my 25+ years of IT leadership, one lesson stands out: transformative success is built not on grand gestures but on consistent, strategic wins. Much like building a championship baseball team, the current AI revolution isn't about swinging for the fences—it’s about steady, practical advancements. Organizations navigating AI adoption often face challenges: * Perceived need for massive infrastructure overhauls * Concerns about security and data privacy * Uncertainty about where to start But here’s the key: Start small. Focus on well-defined problems that deliver tangible, quick wins. For example, deploying tools like Microsoft Copilot for meeting productivity and sales preparation has proven incredibly impactful. Quick AI Wins in Action: - Improved meeting productivity through real-time documentation, action tracking, and follow-ups. - Enhanced sales enablement with AI-driven customer insights, personalized strategies, and competitive intelligence. These small initiatives not only deliver immediate ROI but also build momentum for broader AI adoption. The journey to AI success isn’t about revolutionary leaps—it’s about evolutionary progress. Start with practical, measurable improvements, and let those results drive your expansion. What small AI implementations have you seen drive big value? I’d love to hear your thoughts. https://lnkd.in/g8KQDcfF