Evaluation of Technology Solutions

Agriculture has always been a cornerstone of civilization, but the tools and methods we use are rapidly evolving. Today, the fields of farming are not just tilled by traditional machinery but shaped by advanced technologies that are redefining how we grow, manage, and harvest food. Some examples of how technology is transforming agriculture: 🔍 Targeted Agriculture Gone are the days of blanket approaches to farming. Precision agriculture uses satellite imagery, GPS, and sensor data to pinpoint exactly where resources are needed. This means: - Reducing water, pesticide, and fertilizer waste. - Minimizing environmental impact while maximizing yields. - Allowing real-time decision-making with insights from field-level data. 🤖 Smart Farming Equipment Automation is not just a buzzword, it helps with efficiency: - GPS enabled tractors ensure perfect rows with no overlap, saving time and fuel. - Drones monitor crops, identify diseases, and assess soil conditions, offering a bird’s-eye view of fields in a fraction of the time manual inspections would take. - Robots are now even capable of planting and harvesting with precision, reducing reliance on manual labor. 📊 In modern farming, data is king. Advanced tools allow farmers to collect and analyze information like: - Soil health metrics for optimized planting strategies. - Weather patterns to plan irrigation and prevent losses. - AI powered predictive models that help forecast challenges and improve crop yields. 🌍 With climate change introducing new uncertainties, technology helps mitigate risks: - Precision irrigation systems save water in drought-prone areas. - Climate modeling tools guide farmers in selecting the right crops for shifting weather conditions. - Innovations like vertical farming and hydroponics are enabling food production in places where traditional agriculture struggles. What’s your take? Could these advancements make agriculture one of the most exciting industries to innovate in? #innovation #technology #future #management #startups

After reviewing more pitch decks these past few days, I see African fintech founders are still flogging the dead horse that is "banking the unbanked" as a lazy fundraising pitch. From Yaounde to Cape Town, it’s the same story, another mobile wallet, payments app, another promise to bring financial inclusion to the masses. Truth is: most Africans are not unbanked because they lack access; they’re unbanked because they lack income. A new app won’t change that. The Brutal Truth Lack of Disposable Income – People don’t need more fintech solutions; they need more money. Without increased economic productivity, most “financial inclusion” solutions remain useless. Broken Unit Economics – Many fintechs rely on unsustainable VC fueled growth, acquiring “users” who don’t generate revenue. Regulatory Capture & Infrastructure Gaps – Governments protect banks and telcos dominate mobile money. The real bottlenecks are systemic, not just about "access." Startups often underestimate how slow, expensive, and political it is to scale across markets. Real Problems & Better Solutions Income-Generating Fintech – Instead of just moving money, fintech should help people make money. Platforms enabling gig work, SME financing, and export-focused businesses can drive real financial inclusion. A fintech that helps informal traders access larger markets, rather than just helping them "save." Decentralized Credit & Alternative Lending – Traditional credit models don’t work in Africa. Instead: Use supply chain data, mobile behavior, and transaction flows to build more dynamic credit models. Integrate fintech into cooperative lending structures like tontines or village savings groups, where trust already exists. B2B Payments & Trade Infrastructure – Cross-border trade needs work, killing SME growth. Fix it: Build better escrow and invoice financing tools that help African businesses transact across borders securely. Verticalized Fintech in High-Impact Sectors – Fintech should power real economic activity, not just payments. Agritech fintech: Give farmers access to dynamic pricing, supply chain finance, and better insurance. Healthcare fintech: Enable embedded payments and credit for medical services, helping people afford care without predatory loans. Logistics fintech: Provide financing for truckers, warehousing solutions, and real-time supply chain support. Infrastructure-First Fintech – If power, internet, & ID verification are problems, solve those first. Payments without stable connectivity? Build USSD-based financial services. Weak credit infrastructure? Build platforms that help lenders pool risk and share credit data across borders. The era of cheap fundraising gimmicks is over. African fintech must shift from vanity metrics to real impact, solving income generation, trade inefficiencies, and credit access at scale. I'm tired of saying this, founders who build with these in mind won’t need to beg for funding; investors will come looking for them.

Top questions to ask your IT vendors and partners when they pitch their sustainability products… The questions are aimed at their IT services, looking to understand how they are coping with the challenge, after all, we are all facing the same challenge in reducing digital emissions and we all consume a lot of IT resources! If they find these questions challenging, then maybe they aren’t the right partner to be helping you on your decarbonisation journey! Here’s the list: 1) Have you baselined digital emissions from your IT services? 2) Can you share your baseline report and methodology? 3) What areas of IT did you include and exclude? 4) Which scopes and subcategories did you include/exclude and why? 5) What waste have you identified? 6) What is your emissions reduction roadmap and where are you on it? 7) Who owns the roadmap? 8) What targets and KPIs have you put in place and how are you tracking against them? 9) What’s your net zero target date and are you relying on offsets or similar approaches to reach this target? 10) What have you learnt so far and what has been your biggest win? I personally think the last one is key as it shows how they are learning as they progress on their journey towards sustainable operations. I’d be really interested to see what kind of responses you get!

Understand that the techno-economic assessment of net zero projects isn't: ❌ Just a simple cost calculation ❌ A one-size-fits-all approach ❌ A theoretical exercise with no real-world application ❌ A quick decision-making tool ❌ An isolated technical evaluation But, it is: ✅ A comprehensive strategic framework ✅ A critical roadmap for sustainable investments ✅ A multi-dimensional analysis of technological and economic viability ✅ A key driver for decarbonisation strategies ✅ A systematic approach to climate-aligned project development Your TEA helps to answer these questions: 1. Can this net zero project deliver sustainable economic returns? 2. What are the technological risks and potential mitigation strategies? 3. How does the project align with long-term climate and business goals? Pro tip: Always integrate comprehensive lifecycle cost analysis with robust technological performance metrics. In my years of consulting and research, I've seen how a rigorous techno-economic assessment can transform seemingly impossible net zero projects into groundbreaking sustainable investments. The magic lies in understanding the numbers and the holistic potential of innovative green technologies. Have you ever transformed a challenging sustainability concept into a viable project? What was your most significant learning? #NetZero #Research #ChemicalEngineering #Science #Scientist #Professor #PhD

Most businesses talk about AI transformation. → They attend conferences. → Read whitepapers. → Schedule vendor demos. But here's what 73% of executives won't admit: *️⃣ They're paralysed by the possibilities. Great AI adoption doesn't just automate tasks. → It transforms workflows. → It amplifies human potential. → And you can measure the ROI. Data will show you what's possible, but strategic thinking is what gets you results. 💡 Here's what most leaders keep getting wrong (and can't seem to break free from): – 68% of companies still approach AI as a technology solution rather than a business transformation, despite MIT research showing that workflow decomposition increases success rates by 3x. – 54% of AI pilots fail because businesses skip the cost-benefit analysis, yet Gartner data proves that systematic evaluation frameworks reduce implementation costs by 40%. – Leaders invest 80% of their AI budget in high-stakes applications without human oversight, even though Forbes analysis shows that 85% of successful implementations start with low-risk, quick-payback projects. So, if you're ready for transformation, here's a proven roadmap to break through: → Decompose before you deploy. → Break every workflow into discrete tasks. → Map what's repetitive, creative, or time-consuming using tools like ONET Online. → Run the numbers ruthlessly. → Calculate licensing costs, adaptation efforts, and error correction mechanisms. → Compare against traditional methods. → Accuracy requirements vary—marketing copy can tolerate errors, medical diagnoses cannot. ✳️ Start small, think big. Launch pilots with pre-built solutions, commercial models like GPT-5, or open-source options like DeepSeek. Build human-in-the-loop systems from day one. - Use the 2x2 matrix. - Plot use cases by risk versus demand. - Focus on low-risk, high-demand applications like routine customer inquiries before tackling legal document drafting. This systematic approach helps businesses avoid the common trap of being overwhelmed by AI possibilities and instead focus on use cases that align with their strategic priorities and resource constraints. ↳ Train beyond the data team. ↳ Involve employees across the organisation. ↳ They'll spot opportunities your data scientists miss. Build enterprise-wide AI literacy around concepts like RAG and data quality. At successful companies, they don't separate AI strategy from business strategy. Every implementation serves both. Are you making these fundamental mistakes? - Go systematic. - Balance methodology with bold experimentation. That's how you build AI advantage that competitors can't replicate. ↳ Could it be easier said than done? ↳ Or will it be another missed opportunity? ↳ How strategic will your next AI move be?  Don't let your competitors outmaneuver you.

It does not have to be big factory farms versus small organic ones. There is another way. AI robots fill in for weed killers and farm hands. A solar-powered, AI-driven robot autonomously weeds fields without chemicals. It offers a sustainable solution to labour shortages and herbicide resistance. Designed by former Tesla engineer Richard Wurden, the robot mimics human weeding and runs on sunlight. The robot's AI system takes in data from onboard cameras, allowing it to follow crop rows and identify weeds. Farms are struggling: → Chemical resistance is growing Herbicide-resistant weeds are now found in 101 crops across 72 countries. → Labor shortages are real The share of U.S. farmers reporting labor shortages jumped from 14% in 2014 to 53% during the pandemic—making automation not just a luxury, but a necessity. But here's how AI and robotics can change that: → AI that thinks like a farmer → Powered by pure sunlight → 97% accuracy in weed removal → Zero chemical footprint The Real Impact: → 96% less chemicals needed → Healthier soil microbiome → Stronger crop yields → Sustainable farming at scale BUT one big challenges remains. Commercial entry-level robots often cost around $13,000–$20,000 each. Innovation with new modular robots are being developed for as little as $2,500 to make technology accessible to small and mid-sized farms. While humankind has never before produced as much food, feed and other agricultural produce on this planet, the number of people going to bed hungry tonight has also never been as high as today. Food production is an important contributor to climate change and at the same time is acutely threatened by its consequences.  Follow me Dr. Martha Boeckenfeld for more on Tech impacting our Future. ♻️ Repost to learn about organic farming with technology. #AgTech #Sustainability #FutureOfFood

If you’re leading AI initiatives, here is a strategic cheat sheet to move from "𝗰𝗼𝗼𝗹 𝗱𝗲𝗺𝗼" to 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘃𝗮𝗹𝘂𝗲. Think Risk, ROI, and Scalability. This strategy moves you from "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗺𝗼𝗱𝗲𝗹" to "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝘀𝘀𝗲𝘁." 𝟭. 𝗧𝗵𝗲 "𝗪𝗵𝘆" 𝗚𝗮𝘁𝗲 (𝗣𝗿𝗲-𝗣𝗼𝗖) • Don’t build just because you can. Define the Business Problem first • Success: Is the potential value > 10x the estimated cost? • Decision: If the problem can be solved with Regex or SQL, kill the AI project now. 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗼𝗳 𝗼𝗳 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 (𝗣𝗼𝗖) • Goal: Prove feasibility, not scalability. • Timebox: 4–6 weeks max. • Team: 1-2 AI Engineers + 1 Domain Expert (Data Scientist alone is not enough). • Metric: Technical feasibility (e.g., "Can the model actually predict X with >80% accuracy on historical data?") 𝟯. 𝗧𝗵𝗲 "𝗠𝗩𝗣" 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗩𝗮𝗹𝗹𝗲𝘆 𝗼𝗳 𝗗𝗲𝗮𝘁𝗵) • Shift from "Notebook" to "System." • Infrastructure: Move off local GPUs to a dev cloud environment. Containerize. • Data Pipeline: Replace manual CSV dumps with automated data ingestion. • Decision: Does the model work on new, unseen data? If accuracy drops >10%, halt and investigate "Data Drift." 𝟰. 𝗥𝗶𝘀𝗸 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 (𝗧𝗵𝗲 "𝗟𝗮𝘄𝘆𝗲𝗿" 𝗣𝗵𝗮𝘀𝗲) • Compliance is not an afterthought. • Guardrails: Implement checks to prevent hallucination or toxic output (e.g., NeMo Guardrails, Guidance). • Risk Decision: What is the cost of a wrong answer? If high (e.g., medical advice), keep a "Human-in-the-Loop." 𝟱. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Scalability & Latency: Users won’t wait 10 seconds for a token. • Serving: Use optimized inference engines (vLLM, TGI, Triton) • Cost Control: Implement token limits and caching. "Pay-as-you-go" can bankrupt you overnight if an API loop goes rogue. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 • Automated Eval: Use "LLM-as-a-Judge" to score outputs against a golden dataset. • Feedback Loops: Build a mechanism for users to Thumbs Up/Down outcomes. Gold for fine-tuning later. 𝟳. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 (𝗟𝗟𝗠𝗢𝗽𝘀) • Day 2 is harder than Day 1. • Observability: Trace chains and monitor latency/cost per request (LangSmith, Arize). • Retraining: Models rot. Define when to retrain (e.g., "When accuracy drops below 85%" or "Monthly"). 𝗧𝗲𝗮𝗺 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 • PoC Phase: AI Engineer + Subject Matter Expert. • MVP Phase: + Data Engineer + Backend Engineer. • Production Phase: + MLOps Engineer + Product Manager + Legal/Compliance. 𝗛𝗼𝘄 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗺𝘆 𝗮𝗱𝘃𝗶𝗰𝗲): → Treat AI as a Product, not a Research Project. → Fail fast: A failed PoC cost $10k; a failed Production rollout costs $1M+. → Cost Modeling: Estimate inference costs at peak scale before you write a line of production code. What decision gates do you use in your AI roadmap? Follow Priyanka for more cloud and AI tips and tools #ai #aiforbusiness #aileadership

There’s a new trend among Creative tech startups. I call it the “rise of the Creative SaaS” (Software as a Service). 👋🏾 Goodbye, clunky streaming services, deserted fashion ecommerce sites and gig listing platforms with 12 users. Smart Createch founders across Africa have now caught on that what the industry truly needs is operating systems that standardize workflows, production, payments, logistics and data analytics. ANKA (acquired by Global Shop Group), which transitioned from marketplace to SaaS solution for fashion, crafts and other small entrepreneurs, is the OG of this trend. Some would call SaaS companies unsexy, but not me. Because I like businesses that make sense. Here’s just a few examples of companies building the much-needed rails for Africa’s creative class: 🎬 Filmmakers Mart is a production OS for crews, locations, permits, payroll and set logistics. 🤳🏿 Selar provides a full stack enabling creators to sell courses, memberships, tickets and digital goods, with fast local/FX payouts. 🎧 Makerverse handles rights, splits, distribution, analytics and cross-border payments for independent music labels, aggregators and communities. 👗 Stylebitt is an OS for fashion businesses and independent tailors, digitizing orders, measurements, costing and fulfillment. 🎤 HustleSasa provides ticketing, logistics and cash advance solutions for live events. 🌐 The Folklore helps emerging fashion and beauty brands manage wholesale, dropship, and international shipping so they can sell globally. 🎞️ Fusion Intelligence Technologies (MTN'25) develops cinema software that includes ticketing, box office reporting, as well as community-cinema infrastructure. 💅🏾 Splice helps beauty salons manage bookings, payments, staff, loyalty programs and inventory. All these companies are targeting fragmented supply chains in dire need of standardized tools and user-friendly tech to replace Google spreadsheets or even written records. This may just be the meet-cute moment between creative and tech that we’ve been waiting for. Are you using any of these solutions? What are some other creative sector inefficiencies that haven't been addressed? (let's see if those could be turned into software). ------- Hi, my name is Marie 👋🏽 I’ve been a strategic advisor, investor, and entrepreneur in the African Creative and Sports space for 20 years. I’m also a former TV journalist, a reformed producer, and an enthusiastic speaker and host. For business insights you cannot get anywhere else, join the 10,000+ other professionals who subscribe to my monthly newsletter HUSTLE & FLOW: https://lnkd.in/drBY8jnz

This week’s defining shift for me is that XR is becoming a marketing medium. What once felt like a technology for experimental activations is maturing into a channel for ongoing engagement and sales. Across industries from retail to real estate, XR is transforming how people experience products, events, and spaces, creating immersive experiences that connect with customers and deliver measurable results. This week’s spatial computing news surfaced signals like these: 🍕 Pizza Hut’s AR racing game turns pizza boxes into a campaign platform, letting customers scan a QR code to unlock an interactive Supercars experience tied to the Bathurst 1000. 😎 Banuba’s new eyewear try-on for Shopify gives online retailers an easy way to integrate AR into e-commerce, letting customers “try before they buy” directly from their devices. ⛵ SailGP’s RaceScape XR app blends live video and an AR tabletop racing experience on Vision Pro, making mixed reality part of how the league engages fans worldwide. 🪞 Aircards’ £3M raise will expand AR mirrors, LED tunnels, and spatial analytics, helping brands transform retail and event spaces into measurable, immersive experiences. 🏡 Three Space Lab’s $3M seed round scales VR real estate tours that act as both sales and marketing tools for luxury property developers across global markets. Why this matters: We’re still early, but XR is no longer limited to one-off activations. CPG, sports, retail, real estate, and fashion are all exploring ways to integrate it into their marketing and sales strategies. Once this kind of cross-sector momentum builds, it rarely fades. #realestate #marketing #advertising #brands #CPG #QSR #retail #ecommerce #mixedreality #augmentedreality #virtualreality #XR

Revolutionizing Agriculture with Deep Learning: A New Era of Smart Farming! The future of farming is here—powered by deep learning (DL) and AI. As climate change and population growth strain global food systems, agriculture is embracing cutting-edge technologies to boost productivity, reduce waste, and foster sustainability. From detecting crop diseases with pinpoint accuracy to optimizing water usage, deep learning is transforming every facet of farming. This isn’t just innovation—it’s a lifeline for feeding the planet while preserving resources. 🌾 Key Applications Changing the Game: Disease Detection: AI models like EfficientNet-B7 analyze crop images to identify diseases with 99%+ accuracy, slashing losses by 40%. Precision Agriculture: DL-driven IoT systems reduce water and herbicide use by 50%+, while LSTM networks predict yields with near-perfect precision. Autonomous Robots: From weed-killing drones to fruit-picking bots, AI-powered machines tackle labor shortages and cut costs. Climate Resilience: RNNs simulate weather impacts, helping farmers adapt to droughts and extreme heat. 🔧 Under the Hood: Deep learning thrives on neural networks (CNNs for images, RNNs for weather patterns) and integrates IoT sensors, drones, and edge computing. But challenges like data scarcity and high infrastructure costs remain. The future? Lightweight models like MobileNet for rural farms and federated learning to democratize AI access. 🤝 Let’s Cultivate Change Together! The AI-in-agriculture market is set to hit $5.76B by 2029—but success hinges on collaboration. Governments, tech innovators, and farmers must unite to scale these solutions. Ready to dig deeper into how AI is sowing seeds for a sustainable future? Read this interesting article "Revolutionizing Agriculture: How Deep Learning Cultivates a Sustainable Future". This article explores the pioneering applications of deep learning in agriculture, supported by real-world examples, technical insights, and a vision for the future. Let’s connect and grow this conversation! Subscribe to newsletter AI4IA: https://lnkd.in/dQhz8KrE