Technology Adoption Benefits

Some technologies don’t just solve problems — they give people their independence back. I rediscovered Liftware, and I was genuinely moved by what it can do. It looks simple: a smart handle connected to everyday utensils. But inside, it’s a powerful piece of engineering designed for people with hand tremors (Parkinson’s, essential tremor, and more). Here’s how it works: 🔹 Sensors detect tiny hand movements in real time 🔹 Micro-motors instantly counteract the tremor 🔹 The spoon or fork stays stable — even if the hand doesn’t The result? Up to 70% less shaking. And for many people, that means eating soup again… without help. This is technology at its best: invisible, intelligent, and deeply human. 💡 My take Most people don’t know this, but Liftware was developed by a small startup before being acquired by Google’s life sciences division (now Verily). What makes it remarkable is the engineering challenge: the device doesn’t try to stop the tremor — it predicts and cancels it. It’s basically a tiny real-time AI system… hidden inside a spoon. This is the future I love: not just smarter devices, but more compassionate ones. If you’ve seen other innovations that genuinely improve people’s lives, I’d love to discover them. What’s one piece of tech-for-good that inspired you recently? #techforgood #innovation #technology #healthtech #accessibility #assistivetechnology #futureofhealth #inclusiveDesign #AI #impact

Don't need to comment, like, or connect. Download it. Read it. Use it. Learn with it. Over the last weeks I kept seeing the same pattern. Designers were curious about MCP, but stuck. The path was unclear. Setup felt intimidating. Real use cases were missing. So I built the Design MCP Adoption Toolkit. A practical guide for using MCP inside a real Figma workflow. No theory. No hype. Just execution. Inside you will find: → What MCP is in plain language. → The three MCPs that matter for design work. → The mental model for Anthropic Claude Code to Figma, OpenAI Codex to Figma, and Figma Console MCP by Southleft, LLC and TJ Pitre. → The full setup in nine clear steps. → Nine real workflows you can test this week: Accessibility audits. Ticket validation before handoff. Token migration. Multi platform component handoff. Component documentation generation and more. Our roles are evolving. We are moving closer to that old Webmaster model where design, systems, structure, and technology connect. The designer who understands systems and automation will have leverage. This toolkit is my contribution to that transition. Consume it. Test it. Break things. Ask questions. Explore your own use cases. These are exciting times, and we move faster when we learn together. If you build something interesting with it, share it. Concrete examples help the whole community level up.

WHY AGRICULTURAL RESEARCH OFTEN FAILS TO REACH FARMERS — A Consultant’s Perspective Having worked with dozens of cooperatives, farmer groups, and agrifood projects across Kenya, I’ve seen a pattern that’s hard to ignore: Agricultural research is abundant. Impact on the ground? Minimal. Why? Research is often academic, not practical. Brilliant findings end up in journals, not in farmers’ hands. Most farmers I work with have never seen or heard of the latest research that could transform their yields or earnings. Top-down approaches dominate. Solutions are designed in labs or research stations with minimal farmer involvement. Yet, farmers are the experts of their own environments. Poor extension linkages. Even when good innovations exist, there’s a huge gap between research institutions and grassroots extension systems. As consultants, we often end up "translating" research that should have been made farmer-friendly from the start. No market lens. Research tends to focus on production. But farmers ask: “Will it sell? Is it profitable?” Without market integration, innovation is just theory. Feedback is ignored. Farmers are rarely involved in evaluating what works or doesn’t. We need more participatory learning, less top-down training. From a consultant’s view, the solution is not just more research—but more relevant, inclusive, and actionable research. Let’s invest in: Co-creating with farmers, Bridging research with market realities, Translating findings into practical guides, audio-visuals, and demos, Strengthening extension and private sector partnerships. The knowledge exists. The gap is in the approach. Farmers don’t need more data—they need results. #Agriculture #FarmersFirst #ResearchToImpact #KenyaFarming #AgriConsulting #FoodSystems #ValueAddition #DairyDevelopment #ExtensionServices #AgriPolicy #AfricanAgriculture

Many groundbreaking innovations stem from solving specific, sometimes minor, issues but yield profound impacts. What do you think about this one? These "incremental innovations" drive efficiency, safety, cost savings, and user experiences forward. Take a look at some examples: - Post-it Notes: Born from a failed attempt at a strong adhesive, these sticky notes revolutionized quick note-taking and reminders. - Airbags in Cars: Rather than redesigning vehicles entirely, adding airbags significantly boosted passenger safety and reduced accident fatalities. - Gore-Tex Fabric: By solving the simple problem of staying dry and comfortable, this breathable, waterproof fabric transformed outdoor clothing. - QR Codes: Improving on barcodes, QR codes store more data and offer easier scanning, revolutionizing information sharing and transactions. - Zippers: Replacing buttons and hooks, zippers streamlined fastening clothes and bags with speed and security. - Wheels on Luggage: The addition of wheels to suitcases set a new standard, making travel significantly more convenient. - Penicillin: Beyond its initial discovery, incremental enhancements in production and distribution have saved countless lives through antibiotics. - LED Lighting: The shift from incandescent bulbs to LEDs delivers substantial energy savings and longer lifespans, addressing efficiency and environmental concerns. - USB Ports: Standardizing a universal port for data transfer and charging simplified connectivity across a diverse range of devices. These examples showcase how small improvements can lead to significant advancements in various aspects of our lives. #Innovation #Progress #Efficiency #Safety via @shajapur_mandi_bhav #Technology

The Future of Forest Protection: LiDAR, Drones & AI 🌲 Forests are one of our most powerful climate solutions, absorbing carbon, regulating ecosystems and supporting biodiversity. But managing them at scale has always been a challenge. For decades, forest monitoring meant teams on foot, manually measuring trees with handheld GNSS receivers, slow, labour-intensive and incomplete. That’s changing fast. NatureTech is revolutionising forest management. Today, LiDAR-equipped drones, AI and real-time data processing allow us to scan 25,000 hectares in hours, with unprecedented precision. 🔍 How it works: ⚬ Drones fly over forests, scanning with LiDAR to capture high-resolution 3D data on trees, terrain and carbon storage. ⚬ AI processes millions of data points, automatically detecting tree species, health status and early signs of decay. ⚬ Real-time monitoring enables us to act before problems escalate, whether it’s illegal logging, pest outbreaks or fire risks. What this means for the future of forests: ✅ Carbon Accounting Becomes 100% Transparent Governments and businesses can now track exactly how much CO₂ forests are absorbing. No more estimates, just verifiable carbon data, making carbon credits and nature investments far more credible. ✅ Smarter, More Resilient Forests AI-powered models will soon predict threats before they happen, allowing teams to prevent pest outbreaks, fire risks and tree diseases, before they spread. ✅ Rewilding at Scale Self-flying drones will restore degraded landscapes, not just mapping forests, but actively dispersing seeds and monitoring regrowth, supercharging rewilding efforts. ✅ The End of Hidden Deforestation Every tree cut, every road built, every illegal clearing will be instantly detected, traced and exposed. Transparency will drive accountability like never before. The biggest shift will be that nature will no longer be seen as a passive resource, but as a living, data-rich infrastructure that we can protect, restore, and invest in with confidence. If we get this right, we could be entering a golden era of forest restoration, where tech finally works for nature, rather than against it. (Source: DJI / SLAM LiDAR / Mistra Digital Forest)

Lots of organisations are trialling Microsoft Copilot, but few share the results. Vendors provide glowing case studies, but what about the mixed ones? That’s why I was excited to see a public study from the Office of Digital Government Western Australia. It was more nuanced than the usual rose-tinted vendor stories, offering valuable insights into AI adoption, raising questions about implementation strategies the rest of us can learn from 5,765 licenses deployed: solid sample size for a robust trial 33% adoption rate: Decent for a new, little-understood workplace technology, but hardly transformative The primary use? Summarising meetings & drafting—important but isolated tasks that lack the integration needed for broader impact. Copilot is doing work that might otherwise not get done, but it’s not yet the game-changer AI could be Observations: Limited integration: Meeting summaries and drafts are isolated activities. Without connecting tools to broader workflows, the potential for transformative value is lost Lack of process analysis: A comprehensive process review was recommended but appears not to have been done. Dropping tools into workflows without context limits ROI Adoption gaps: Why did only 33% adopt when meetings are universal? Barriers—technical, cultural, or support-related—likely played a role Training matters: People who undertook more than one type of training (eg workshops, peer learning, self-paced modules) showed much higher adoption rates. Varied, ongoing training is essential to building confidence and capability Technical limitations: Issues with Excel & Outlook and inaccuracies hurt productivity. Familiarity bias toward enterprise platforms like Microsoft might not always serve users best Prompt engineering struggles: Challenges with prompts suggest gaps in training or change management rather than tool design Over-reliance risks: Concerns about losing deep knowledge are valid. Organisations must balance efficiency with accountability and critical thinking Early adopter bias: Early users were perceived as more productive, which may foster resistance or fear—a common hurdle in change management If you’re planning a trial: Invest in varied training: Training shouldn’t be a one-off. Use diverse formats and reinforce adoption over time Choose fit-for-purpose tools: Don’t default to familiar vendors. Smaller, more agile tools can often deliver better results Conduct a discovery phase: A thorough process review ensures tools align with organisational needs, reducing risks and maximising ROI Set clear metrics: Measure costs, benefits, and adoption outcomes to guide experimentation and ensure accountability If your organisation is running a Copilot trial, or considering one, these steps can help you maximise success. And of course, you can always come talk to us at Lithos Partners. You knew that, right? Have you worked with AI tools like Copilot? I’d love to hear your experiences or tips for successful adoption.

It's not sexy to say, but most of AI transformation has nothing to do with AI. There are 10 steps in the sequence of making an internal process or external product AI-native. Only 1 step is AI, and ironically, the other 9 steps are the far harder part. Step 1: Identify the problem - Find the manual process worth automating. turn your brain off autopilot & turn on your "suck meter". - Funny enough, your company becomes more efficient just by mapping out your processes even if you don't introduce AI. Step 2: Understand the workflow - Map how people actually work today. grab an 8.5x11 piece of paper or Excalidraw and create a flow chart of the workflow from beginning to end. - Least sexy part, but generally where the people driving transformation (FDE, GTM engineer, etc) should spend the majority of their time. Step 3: Collect the data - Gather sample inputs, documents, edge cases - Example: for my content machine ai workflow, I gathered past slack messages/notion transcripts to test automated ideation Step 4: Build the prototype [The AI Part] - Whether its engineer-led or SME-led the goal is to test your hypothesis that there's a better way of doing things for yourself as customer zero. Don't worry about code cleanliness, don't worry about scalability. Step 5: Test & iterate - Before you take the process from single player (only you using it) to multiplayer (many users), you want to beat it up with as many rounds of work & feedback + edge cases as possible. Turning every process into a self-improving loop before scaling is key. Step 6: Integrate with systems - Point-in-time data is good for testing the workflow, but live data is necessary before going into production. Step 7: Roll out & train - Whether the new process lives on a live link, on GitHub or an internal library, next step is hand-holding your peers/users through the onboarding process of your new workflow/product. Step 8: Drive adoption - Embed the workflow in your culture where adoption is tracked, ideas & feedback are celebrated, and new/creative use cases become social currency in your business. Step 9: Empower contribution - Treat your new process like an opensource project. Allow users to become contributors. Whether they are literally pushing code or are simply empowered to add ideas/feedback to a kanban board that gets serviced by engineers, make everyone feel like a builder. Step 10: Measure & capture value - If you're in the experimental phase of AI adoption in your company, fuck ROI. The goal is to empower people to throw a lot of shit at the wall & see what's worth focusing on. You don't need to be scientific during this process. - If you're in the scale-up phase of AI in your business, and you need to realize hard ROI, you need to reskill employees attached to this process, undershoot your approved hiring roadmap, or measurably increase ACV/conversion rate/sales cycle speed.

There’s a huge difference between ‘I got AI to do this amazing thing for social media points’ and ‘I got AI to do this thing that generates a lot of revenue for my business or our clients.’ Real-world AI is very different. Most agents require small language models. Large context windows and multiple rounds of model calls turn the unit economics of foundational models negative for many use cases. Everything we build for clients starts with local AI. We spend no more than 2 days trying to get the workflow running on the Dell Pro Max T2 in my office. If it won’t run locally, using a frontier model rarely changes that. We scale the agent to support a small set of early adopters. This phase is critical. An early adopter cohort has been trained to use agents at their earliest maturity phase. Most users would reject the agent in this raw form. But this phase is intended to rapidly improve the agent’s workflow integration, orchestration, and reliability. Human feedback from trained early adopters improves agent performance faster than any other approach I have found. We iterate on more than just the LLMs. This phase fills in the knowledge graph, improves tool usage, adds guardrails, and informs the usage of more traditional machine learning models to augment the agent. When improvements plateau, we assess the agent. It is only promoted if its impact on outcomes meets user or customer expectations. Is it valuable? How does it reorchestrate workflows? Can the business monetize it? We roll the agent out to an alpha release cohort to scale the feedback flywheel. At this point, we know we have something valuable. We’re trying to improve its reliability and handle more workflow variations before a wider launch. We only evaluate frontier model usage at this phase. We finally know enough to make targeted decisions about where in the workflow frontier model performance could make a big enough difference to be worth considering. The alpha release also reveals adoption barriers for the agent and reorchestrated workflow. Most agents require us to craft an adoption journey for users and customers. That typically includes training for internal users and a phased rollout for customers. When improvement plateaus again, the agent is ready for general release. The process takes 2-3 months, and only about 30% of the workflows we try in my office end up going the distance. Data and information architecture make a huge difference. One client with a very mature knowledge graph is seeing a workflow success rate of over 50%. Small models perform significantly better for their use cases. #DellProMax

When an unseasonal frost threatened Saraswati Vishwakarma's potato crop, she had hours to decide. Months of work and her family's income were on the line—and her husband was away. The nearest agricultural advisor served thousands of farmers across the region. She turned to FarmerChat. In India, one extension worker often serves more than 5,000 farmers. When disease hits or rains come late, help can take weeks to arrive. That's a wait most smallholder farmers simply can't afford. FarmerChat, an AI-powered tool developed by Digital Green and supported by The Rockefeller Foundation, delivers hyperlocal agricultural advice in farmers' own languages—in real time, on their phones. More than 1 million installs. More than 10 million queries answered. Seven in ten users report applying the advice within 30 days. The technology matters. What matters more: farmers like Saraswati now have something closer to a personal advisor—available exactly when it counts. Read more about how FarmerChat is bridging the information gap for India's farmers: https://lnkd.in/eNmMb4hT

New Publication!! 🌍 Overcoming barriers to the adoption of water-saving technologies in Jordan: policy pathways for transforming knowledge, attitudes, and practices💧 Authors: MAHA Al-ZU'BI, Ph.D. Nafn Amdar Youssef Brouziyne Jordan is facing a severe water scarcity crisis, worsened by rapid population growth, climate change, and the overuse of limited groundwater. With per capita water availability at just 61 m³/year—far below the global threshold of 500 m³/year—it’s one of the most water-scarce countries in the world. 🌿 The agricultural sector, which consumes nearly 48% of the country’s freshwater, is hit especially hard. The reliance on inefficient irrigation methods has led to low water productivity, particularly in the highlands, where productivity is only JOD 0.36 per m³, far below the potential achievable with Water Saving Technologies (WSTs). 💡 However, several barriers hinder the adoption of these critical technologies: - Financial Constraints 💸 - Limited Extension Services 📚 - Technical Gaps 🔧 - Unequal Access, especially for smallholders and marginalized communities 🚜 Many farmers struggle to integrate WSTs into their practices without proper guidance and support. Aligning farmers' knowledge, attitudes, and practices (KAP) with water conservation goals is key to ensuring the successful adoption of these technologies. 🌱 To address these challenges, a multi-faceted approach is required: 💧Research & Tailored Support: Researchers can pinpoint adoption barriers, while practitioners offer targeted guidance to overcome them. 💧Policymaker Action: Policies should encourage WST adoption through financial incentives, education, and research. 💧Education & Awareness Campaigns: Farmers need to understand the long-term benefits of WSTs for sustainable farming. 💧Financial Support: Subsidies or low-interest loans can help make these technologies more accessible, especially for smallholders. 💧A Farmer-Centric Approach: A Market Systems Development (MSD) strategy can improve the market system surrounding WSTs, while peer learning and strong extension services offer ongoing support. By tackling these issues, we can ensure long-term water security and agricultural productivity for Jordan. Together, we can drive the adoption of water-saving technologies and pave the way for a more sustainable future. 🌱 #WaterSecurity #Agriculture #Sustainability #Jordan #WaterSavingTechnologies #ClimateChange #Innovation #WaterConservation #AgricultureSustainability #FutureOfFarming #MarketSystemsDevelopment International Water Management Institute (IWMI) Read full Policy Brief: https://lnkd.in/epr2fWpT