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

When IBM CEO Arvind Krishna says quantum computing is "three to five years away from shocking people," it's time to sit up and take notice. I shared earlier highlights of the AI portion of Arvind's discussion with Malcolm Gladwell. They also went into #quantum, which is getting closer to becoming a reality for enterprises. 3-5 years is 2028-30 -- not so far into the future. And when he says "shocking," he's talking about: - Making billions in the financial markets. Appr. $13 trillion moves through the financial markets each day. Even a 1 basis point improvement is $130 billion. Now you see why the recent paper by HSBC that "using quantum computer, bond trading was 34% more accurate than their prior technique" caused a ripple. - Dramatic improvements in operational efficiency. "Let's take a post office in a mid-sized country [that] ... burns 1 billion gallons of fuel per year." Optimizing last-mile delivery is the classic #travelingsalesman problem. But we can currently only get to 80% efficiency. With quantum, if we can get another 10%, that's 100M gallons of fuel, which could drive hundreds of millions in savings. "These are pretty attractive problems to go after." Indeed. Today's challenge is scale. "Quantum computing today is where GPUs and AI were in 2015." But if your business has challenges that are solvable with quantum, and you're not starting to experiment with it now, you may risk being "out of business in 10 years." Quantum represents a "new kind of math" which enables us to solve "new kinds of problems." For retailers and brands, I think about three things: - Merchandise planning at the item x store level that's accurate to within 1-2% of demand. - Personalization at scale that actually delivers on the 1to1 promise we've been chasing for 25 years. - The ability to monitor E2E (farm to home) supply chains and dynamically trigger IFTTT actions with little-to-no human intervention. (and i'm sure there are many others) Arvind sees quantum as being "equal to semiconductors" in the ranking of technology advancements of the past 150 years. Yet it's barely part of today's conversations. The internet had its #NetscapeMoment. With AI, it was the launch of #ChatGPT. Quantum will hit its #TippingPoint as well ... and it will probably happen sooner than we think. YouTube video -- https://lnkd.in/eE9FdEU3 IBM Smart Talks -- https://lnkd.in/eHhgTF7z (also available on all major #podcast platforms)

With electricity demand surging, the U.S. transmission system is approaching its limits. Yet building new lines often takes 5 to 15 years due to permitting, environmental reviews, and land-use constraints. ⚡️Reconductoring offers a faster, lower-impact alternative. By upgrading existing lines with advanced conductors like ACCC or ACCR, utilities can double or even triple capacity—without building new towers or acquiring new rights-of-way. These high-temperature, low-sag (HTLS) conductors use materials such as carbon fiber to minimize sag and maximize throughput. 👉🏽 Why it matters: * Up to 3x current-carrying capacity using existing infrastructure. * Deployment in 18 to 36 months—far quicker than new construction. * 98% of U.S. transmission lines are viable for reconductoring. GridLab estimates reconductoring alone could provide over 80% of the additional transmission capacity needed to reach U.S. clean electricity goals by 2035. Yes, challenges like precision tensioning, splicing, and structural assessments remain, but they’re manageable with current tools, standards, and workforce skills. This is a proven, scalable solution that deserves greater attention. What’s your take? 👇🏽

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.

Last week at LegalTechTalk, I moderated a closed-door roundtable on AI with 10 innovation leaders from leading law firms across the US, UK and Europe. Here are my top 3 key takeaways from the conversation: 1/ Targeted use cases over firm-wide rollout - Firm-wide rollouts rarely result in massive adoption. One US-headquartered law firm shared that after their initial firm-wide rollout of Gen AI, they only had adoption from 10-20% of the firm. - There is a spectrum of tools emerging on the market from general-purpose microwaves to specialised pizza ovens and expert personal chefs. It’s about knowing which kind of tool is best suited for which use case and context. - Focusing on targeted and concrete use cases is the best way to prove value by showing (instead of telling) success and getting wider buy-in across the firm, and to bring on board sceptics. 2/ We sometimes forget that lawyers are humans - It’s difficult to get lawyers to leave the tools they are already working with and are familiar with. One innovation leader shared that vendors who are embedded into Outlook and Word make it easier to drive adoption. - Creating a network of AI Champions and focusing on peer-to-peer learning with knowledge sharing and social learning is a better way to create energy and convert colleagues to use AI instead of endless training sessions. - Most of the firms shared that there were AI sceptics inside their organisation. A large part of their role is consensus-building and navigating the politics of the partnership. It’s a far better use of resources to work with the willing and win the naysayers with proof of success. - AI fatigue and overwhelm are real. It’s impossible to run pilots with lawyers to test every single vendor on the market. It’s more effective to filter down on a specific use case in a practice group and find vendors who can solve that problem. 3/ Vendor Proliferation - A sea of sameness? - There is an endless list of vendors flogging AI in legal to innovation teams, meaning that the signal isn’t clear from the noise. - Most of the vendors have massive overlaps in features and capabilities, leading to redundancies in purchasing multiple solutions. It also raises questions for innovation leaders about what value vendors add above the core LLMs they use. - One innovation leader at a billion-dollar law firm shared that vendors need to think about decoupling back-end AI functionality from the front-end interface so firms can purchase what they need from different vendors and construct a unified single interface for lawyers. The roundtable was sponsored by DeepJudge. Massive thanks to Timothy Sulzer and Lukas Reichart for sharing their perspectives and to the team at LegalTechTalk for the opportunity to moderate the discussion. What would you add?

Even when charged from relatively “dirty” power grids, battery-electric vehicles (BEVs) outperform internal combustion engine (ICE) vehicles on lifecycle emissions. In key gobal regions studied — from the U.S. and China to the UK, Germany and Japan — EVs come out cleaner over their entire lifetime. Yes — manufacturing EVs (especially batteries) remains emissions-intensive. But once on the road, BEVs rapidly recoup that initial “carbon debt.” Over 250 000 km of driving, a medium-sized BEV’s CO₂ footprint can be 21–71% lower than the equivalent ICE car — depending on driving patterns and the energy mix. That matters — we can’t afford near-term paralysis based on imperfect grids or “worst-case” assumptions. As grids continue to decarbonise, the environmental advantage of EVs will only grow. If we want to accelerate transport decarbonisation at scale, the message is clear: EV deployment must go hand-in-hand with cleaner grids — but delaying electrification until perfect conditions are met is a luxury the climate doesn’t afford.

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