Data-Driven Innovation Analysis

Had to share the one prompt that has transformed how I approach AI research. 📌 Save this post. Don’t just ask for point-in-time data like a junior PM. Instead, build in more temporal context through systematic data collection over time. Use this prompt to become a superforecaster with the help of AI. Great for product ideation, competitive research, finance, investing, etc. ⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰ TIME MACHINE PROMPT: Execute longitudinal analysis on [TOPIC]. First, establish baseline parameters: define the standard refresh interval for this domain based on market dynamics (enterprise adoption cycles, regulatory changes, technology maturity curves). For example, AI refresh cycle may be two weeks, clothing may be 3 months, construction may be 2 years. Calculate n=3 data points spanning 2 full cycles. For each time period, collect: (1) quantitative metrics (adoption rates, market share, pricing models), (2) qualitative factors (user sentiment, competitive positioning, external catalysts), (3) ecosystem dependencies (infrastructure requirements, complementary products, capital climate, regulatory environment). Structure output as: Current State Analysis → T-1 Comparative Analysis → T-2 Historical Baseline → Delta Analysis with statistical significance → Trajectory Modeling with confidence intervals across each prediction. Include data sources. ⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰

#TeachMeTuesday How much innovation are we missing if we only look at the R&D line in financial statements? 👉 Result: a lot! 📄 In a newly published Research Policy article (👉 https://lnkd.in/enDMWuVA), Neophytos Lambertides, Marina Magidou, and Anna Emilia Maruska, Ph.D. , the authors ask a simple question: what if part of the innovation signal is hiding in plain sight — in what firms say, not only in what they report numerically? Evidence in the paper then shows that over 10% of firms reporting zero or missing R&D expenditures nonetheless exhibit clear signals of innovation activity. 🧠 What this implies for innovation measurement The results reinforce a broader message: innovation is increasingly intangible, distributed, and poorly aligned with traditional reporting categories. Relying narrowly on reported R&D risks underestimating innovation — especially in services, digital-intensive sectors, and firms innovating through processes rather than products. 🏛️ What could better measurement look like? Building on the paper’s contribution, three complementary directions stand out: 🔔 Systematic use of narrative disclosures to capture latent innovation activity beyond formal R&D 🔔Broader “total innovation investment” concepts, combining R&D with other innovation-relevant intangible expenditures 🔔 Richer output indicators, integrating patents with trademarks, designs, and market-based innovation signals 🌍 Link to global policy work These insights align closely with the measurement philosophy of the WIPO, which combines traditional and non-traditional indicators to better capture innovation in all its forms.

I've been playing with the latest Gemini 3 model in AI Studio... this is prompt output explaining its agentic capabilities to me... the example may open a debate about why companies continue to calculate LTIFR but that aside.. Here is a concrete example of how a Safety Manager could use an agentic workflow to turn hours of manual spreadsheet work into a 2-minute task. The Scenario: "The Monthly Board Report" Imagine it is the end of the month. You have a raw CSV file export from your incident reporting software containing 500 rows of data: near misses, first aid treatments, and lost time injuries. You need to calculate your LTIFR (Lost Time Injury Frequency Rate), identify trends, and write a summary for the executive leadership team. How the "Safety Analyst Agent" handles this: You upload the raw CSV file and type "Here are the incident logs for this month. Total hours worked were 45,000. Calculate our monthly LTIFR, tell me which body part was most frequently injured, and generate a chart showing the trend of 'Slips, Trips, and Falls' compared to last month." Here is the agentic process that happens behind the scenes: 1. Tool Use: Data Cleaning & Calculation (Python) Instead of hallucinating a number, I (the agent) write and execute a Python script to process your specific file. I filter the data: I scan the "Severity" column to isolate only the "Lost Time Injuries" (LTI). I apply the formula: I calculate the LTIFR using the formula: (LTI×1,000,000) / Total Hours I clean the text: I standardize entries like "L. Hand" and "Left Hand" into a single category to give you accurate stats. 2. Reasoning: Trend Analysis I don't just count; I look for patterns. I compare this month's "Slip/Trip" count to the previous month's data (if provided) or the rest of the year's average. Agent Insight: I might notice a correlation you didn't ask for, such as, "Note: 60% of hand injuries occurred on the Tuesday night shift." (this assumes data quality) 3. Tool Use: Visualization I use a plotting library (like Matplotlib) to generate a professional bar chart or line graph actual image file that you can drag and drop directly into your Word doc or PowerPoint. 4. Final Output I present the final response: LTIFR: Your rate for this month is 3.2, which is a 15% decrease from last month. Top Injury: "Fingers/Hands" (4 incidents). Trend Alert: While overall incidents are down, "Slips" in the Warehouse zone have increased by 2 incidents. Draft Executive Summary: "This month saw a positive downward trend in our overall frequency rate. However, a localized spike in warehouse slips suggests we need to review housekeeping protocols in Zone B..." Why this is "Agentic" A standard AI would just chat with you about safety theory. An Agentic AI actually did the math, drew the graph, and audited the data for you, acting like a junior safety analyst sitting at your desk. Example aside. This is the direction we're headed! Thoughts? #safetytech #safetyinnovation

Why your next big idea might be closer than you think. Most founders chase shiny objects. I mine existing assets. The Proximity Principle: Your biggest opportunity isn't in the next industry. It's in the current conversation you're not having. The Pool Revelation: I was floating, thinking about my business. Realized I had 12 clients paying $50K each. All asking the same follow-up question. All needing the same next step. That question became a $180K product. Built in 2 weeks. From my existing knowledge. The Hidden Goldmine Framework: 1. The Client Question Audit What do your clients ask AFTER they hire you? That's your next offer. 2. The Complaint Pattern What do they complain about in your industry? That's your competitive advantage. 3. The Referral Request Who do they ask you to recommend? That's your partnership opportunity. 4. The Problem Evolution What problem emerges once you solve their first problem? That's your upsell. The Existing Asset Inventory: Look at what you already have: → Client conversations (goldmine of insights) → Email responses (templates waiting to be packaged) → Voice messages (frameworks hiding in plain sight) → Pool thoughts (strategies you take for granted) The Innovation Myth: You don't need a breakthrough idea. You need to notice what's already working. The $180K Example: Clients kept asking: "Now what?" After I fixed their personal brand, they needed systems. After systems, they needed team training. After training, they needed ongoing strategy. I turned "Now what?" into "Here's what's next." Each step became a new revenue stream. The Proximity Strategy: Instead of asking "What's the next big thing?" Ask "What's the next logical thing?" Instead of "What market should I enter?" Ask "What need am I already serving?" Instead of "What should I build?" Ask "What am I already building?" The Resource Reality: You have more assets than you realize: → Your client conversations contain frameworks → Your email responses contain templates → Your problem-solving process contains systems → Your natural way of thinking contains IP The Innovation Process: 1. Document what you're already doing 2. Package what you're already saying 3. Systematize what you're already solving 4. Monetize what you're already creating The Closer-Than-You-Think Examples: → Your onboarding process = A course → Your client check-ins = A membership → Your problem-solving method = A framework → Your decision-making process = A consulting offer The Pool Time Advantage: My best ideas don't come from brainstorming. They come from reflecting on what's already working. What patterns am I seeing? What questions keep coming up? What problems keep appearing? What solutions keep working? The Innovation Insight: Innovation isn't about creating something new. It's about seeing something that's already there.

"We've always done it this way." Five words that quietly kill innovation before it even starts. I watched a team spend six months digitizing their approval process—only to realize they'd automated something that didn't need to exist in the first place. They'd moved a paper form online, added digital signatures, and celebrated the "transformation." But nobody had asked the fundamental question: Why do we need approval for this at all? ⸻ Here's what real transformation looks like: - It's not digitizing your existing processes. - It's questioning whether those processes should exist. Consider a client whose finance team was entangled in a complex payment process with numerous steps, only to discover the process was designed to work around an existing system’s limitation that will no longer exist in the new platform.  By asking “why”, it will uncover and minimize gotchas. The moment we stepped back and asked "why," everything changed. Layer after layer of workarounds and assumptions that nobody questioned. All built around constraints that had disappeared years ago. ⸻ The breakthrough came when we stopped asking "How can we make this faster?" and started asking "Should we be doing this at all?" That shift in thinking? That's where innovation lives. ⸻ Here's how to identify and break free from legacy thinking: ↳ Challenge every "required" step Ask who requires it and what happens if you skip it ↳ Map the real workflow, not the official one Watch how work actually gets done—that's where the truth lives ↳ Hunt for phrases like "we have to" or "they need" These often hide unexamined assumptions ↳ Bring in someone who's never done the job They'll ask questions veterans stopped asking years ago ⸻ The most dangerous phrase in transformation isn't "This won't work." It's "This is how we've always done it." What process in your organization exists simply because it always has? ♻️ Repost to share with your network. ➕ Follow Janet Kim for more tips ~~~~~~ 📩 Want more strategies like this? Subscribe to Level Up Weekly - link in the Featured section. ~~~~~~ I leverage 19 years in Stanford tech to help emerging leaders think strategically, build influence, and execute with confidence, so you’re seen, heard and valued.

Hidden in government laboratories across the UK sits a treasure trove of breakthrough innovations that could transform industries - but most never see the light of day. Ploughshare, the Ministry of Defence's commercialisation arm, has quietly been unlocking this potential for nearly two decades. From handheld devices detecting traumatic brain injury on rugby pitches to hydrophobic coatings now protecting consumer footwear, they're proving that military research has far broader applications than most imagine. The challenge isn't lack of innovation - it's bridging the gap between proof-of-concept and market reality. Government scientists typically focus on solving specific problems rather than commercial viability, leaving exceptional IP stranded in laboratories. Ploughshare's approach is methodical: identify promising inventions, assess their impact potential beyond defence, then either license to existing companies or create spin-outs. They've commercialised over 140 technologies, generated £126 million in economic value, and created 500+ jobs. What's particularly striking is the diversity of applications. Naval sonar research becomes underwater infrastructure monitoring. Chemical threat protection becomes waterproof footwear. Military camera technology transforms industrial inspection capabilities. The real opportunity lies in re-examining past research with fresh perspectives. AI is now helping revisit failed trials, uncovering why they failed and enabling successful redevelopment. For the UK's innovation ecosystem, this represents untapped potential at scale - taxpayer-funded research delivering broader economic and social impact. #DefenceInnovation #TechTransfer #UKInnovation #Commercialisation

𝗠𝗮𝗽 𝗧𝗼𝗱𝗮𝘆’𝘀 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲 𝘁𝗵𝗲 𝗣𝗮𝗶𝗻 𝗣𝗼𝗶𝗻𝘁𝘀: 𝗨𝗻𝗰𝗼𝘃𝗲𝗿 𝗛𝗶𝗱𝗱𝗲𝗻 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻 𝗕𝗲𝗳𝗼𝗿𝗲 𝗔𝗱𝗱𝗶𝗻𝗴 𝗧𝗲𝗰𝗵 Every successful transformation starts by seeing your current state with crystal clarity. Too often, we rush to evaluate software features before understanding how work really flows and where it grinds to a halt. Imagine treating your processes like a road trip: you wouldn’t choose a new vehicle until you know which roads are blocked. The same goes for systems. A mid‑market manufacturer struggled with late shipments. Leadership blamed their ERP’s lack of functionality, but frontline teams knew the truth: manual handoffs and conflicting spreadsheets created bottlenecks. In addition, 40% of delays stemmed from manual cross‑checks between dispatch and finance, a step invisible on org charts but glaring on the shop floor. By facilitating honest, workshop‑style mapping sessions (complete with sticky notes and whiteboards), they uncovered redundant approvals and invisible handoffs that no feature list could solve. Involving the people who do the work isn’t optional; it’s essential. Their day‑to‑day insights highlight subtle delays, workarounds, and “exceptions” that hide in plain sight. An unbiased facilitator ensures every voice is heard and prevents solutions from being biased by existing hierarchies. The result? A process map that reveals root causes, not just symptoms, and creates a shared baseline for improvement. By critically analyzing your current state, you build a precision roadmap: automate the highest‑impact tasks, redesign workflows to remove dead ends, and close compliance gaps before they escalate. This targeted, human‑centric approach avoids wasted investment, earns frontline trust, and lays the groundwork for sustainable process improvement.    Once you’ve charted reality, you can make targeted changes, whether that’s simplifying an approval step, automating a data transfer, or selecting a tool that fits the way your teams operate. This honest approach prevents costly rework and builds trust across the organization. Ready to uncover hidden friction and chart a focused transformation path? With Digital Transformation Strategist, let’s discuss how a structured pain‑point diagnosis can drive your next wave of operational excellence. #digitaltransformation #operationalexcellence #processimprovement #processmapping #changemanagement

Most companies drown in data, yet miss what matters. They react to trends only after they’ve peaked. By the time something shows up in the Gartner Hype Cycle, the real opportunity has already moved on. The problem is timing. Weak signals are too early for most to take seriously and act on. And established trends are too late to lead with. So, the solution is signal intelligence, not more data. What we need is detecting early indicators with just enough strength, across diverse and trusted sources, before the crowd catches on. That’s why together with colleagues from FAU and GfK we once developed the Innovation Signals Triple Diamond model. It’s a machine-learning-powered approach to detect signals of change early, systematically, and with minimal noise. It reduces guesswork and increases foresight to create a real competitive edge. How are you tracking early signals today? #foresight #innovationsignals #corporateforesight #machinelearning

When you think of a BA, you probably picture someone gathering requirements, writing documentation, conducting stakeholder meetings, and ensuring solutions align with business needs. That’s still true – but AI has changed the game. Let me explain practically, with examples, how a BA who embraces AI outperforms a traditional BA in terms of productivity, speed, and impact. 1️⃣ Requirement Gathering & Analysis Traditional BA: Spends hours manually writing notes during meetings, transcribing them, and later organizing them into requirement documents. AI-Driven BA: Uses tools like Fireflies.ai or Otter.ai to auto-record, transcribe, and summarize stakeholder discussions in real-time. Then leverages ChatGPT or Claude to instantly convert meeting notes into BRDs, user stories, and acceptance criteria. ⏩ Time saved: 4-6 hours per workshop → down to 30-45 mins. 2️⃣ Data Analysis & Insights Traditional BA: Pulls raw data from SQL/Excel, applies formulas, creates pivot tables, and spends hours interpreting patterns manually. AI-Driven BA: Feeds the same dataset into AI-powered analytics tools (e.g., Power BI with Copilot, Dataiku) to get instant trend analysis, anomaly detection, and visual dashboards. ⏩ Time saved: A task that used to take 2 days → reduced to 3-4 hours. 3️⃣ Process Documentation & Diagrams Traditional BA: Creates process flows in tools like Visio or Lucidchart manually – a time-consuming process requiring multiple review cycles. AI-Driven BA: Uses Whimsical AI or Miro AI where you describe a process in text, and AI auto-generates workflows, swimlanes, and even SIPOC diagrams, editable in seconds. ⏩ Time saved: 50-70% on documentation effort. 4️⃣ Impact Analysis of Change Requests Traditional BA: Reads through large requirement docs, checks dependencies manually, consults multiple teams before documenting impact. AI-Driven BA: Uses AI search and knowledge agents trained on project documentation to instantly highlight affected modules, impacted data fields, and dependent systems. ⏩ Productivity gain: Faster decision-making → reduces analysis time from days to hours. 5️⃣ Testing & UAT Support Traditional BA: Writes test cases manually and reviews test coverage for completeness. AI-Driven BA: Uses AI test generation tools (e.g., Mabl, TestCase Studio AI) to auto-generate test cases and scenarios based on requirements, reducing errors and improving test coverage. ⏩ Time saved: Up to 40-50% in test preparation.💡 The Bottom Line Traditional BA = Manual effort, repetitive documentation, slower delivery. AI-Driven BA = Augmented intelligence, faster deliverables, higher accuracy, more time for strategic thinking. The future of Business Analysis isn’t about replacing BAs with AI. It’s about replacing repetitive BA tasks with AI so that BAs can focus on stakeholder engagement, problem-solving, and delivering business value faster. ✅ If you’re a BA today, start learning AI tools now – not tomorrow. BA Helpline

Rediscovering Your Hidden Advantage: Why What’s Working May Be Your Greatest Untapped Opportunity In business—and in life—we often obsess over what’s broken. We fixate on the inefficiencies, the declining KPIs, the fires that need putting out. But what if the real breakthrough isn't buried in the dysfunction? What if it’s sitting quietly in what already works—but we’ve stopped noticing? “Your biggest opportunity isn't hiding in what's broken; it's hiding in what's working that you've stopped noticing.” This quote strikes at the heart of a common blind spot in strategic leadership: the tendency to overlook consistent success because it no longer feels urgent or exciting. The Success Trap High-performing processes, teams, or products can become invisible over time. When something operates seamlessly, it fades into the background of our attention. We assume it’ll always work. But in doing so, we may miss massive opportunities to scale, replicate, or evolve these silent performers into category-leading differentiators. Take Amazon’s Prime membership. Initially a logistics tool to lock in customer loyalty with free two-day shipping, it evolved into a platform for entertainment, cloud trials, and exclusive shopping. Amazon didn’t fix a problem—they maximized what already worked. (Source) Audit What Works As a leader, I often challenge leadership teams to ask: - What product or service consistently performs above average? - Which team or unit delivers reliable results without drama? - Where do customers express the least complaints and the most loyalty? - Then: What would happen if we doubled down here? Netflix didn’t beat Blockbuster by fixing what was broken—it leaned into the parts of the entertainment experience that people loved and enhanced them with digital convenience. Practical Takeaways Run a “Bright Spots Audit” – Once a quarter, identify the top 3 performers in your business. Not just individuals, but systems, products, or customer segments that quietly drive value. Assign Innovation Resources – Don’t just focus your R&D on underperforming areas. Allocate time and budget to supercharge what’s already strong. Celebrate and Learn – Success stories are just as instructive as failure analyses. Make time to understand why something is working and whether it can scale. Final Thought In a world that rewards urgency, stillness often hides opportunity. Pay attention not just to the squeaky wheel—but to the silent engine that’s been quietly propelling you forward. There may be more power—and profit—there than you think.