Disruptive Technology Assessment

Introducing the A.I. Disruptive Index – What applications will survive Goal: A Qualitative Framework for Identifying AI Opportunities in SaaS Tools This framework helps assess how susceptible a product or industry is to AI disruption. It enables qualitative scoring of incumbents and markets based on two categories: 1) AI Opportunity Factors — areas where AI can accelerate value. 2) AI Constraints — factors that may inhibit or delay adoption. *Opportunity Factors* 1. Accelerates the Data Flywheel Effect Applications that increase in value as more data is captured can greatly benefit from AI. Many SaaS tools are little more than specialized databases (e.g., CRM, work tracking). "AI-first" software goes beyond passive storage—it actively extracts value from data and uses it to improve outcomes over time. 🟥 Static data viewed by users 🟧 Data re-presented for insights 🟨 New data generated from usage 🟩 Generative output created without human input 2. Agentic Behavior and Automation Traditional software reacts to user input. AI-first software initiates actions, reducing time from insight to execution. The more deterministic the path from insight to action, the more ripe it is for automation. 🟥 User-initiated actions only 🟧 AI suggests, user executes 🟨 AI acts under supervision, per rules 🟩 AI acts independently to optimize outcomes 3. Scaling Human Effectiveness AI isn’t just about saving costs—it’s about unlocking new outcomes. AI can enhance productivity or reimagine workflows entirely in domains where human expertise greatly impacts success. 🟥 AI ignored 🟧 AI assists current processes 🟨 AI augments via generative features 🟩 AI redefines workflows and results *Constraints and Readiness Blockers* 4. Data Suitability for Generative AI LLMs excel with interpretive content (like product descriptions), but not with fact-based, error-sensitive information (like event schedules or legal terms). Not all domains have data that lends itself to generative treatment. 🟥 Input is not in AI-suitable formats 🟧 Input is factual and non-generative 🟨 Mix of factual and interpretive input 🟩 Fully interpretive, creative-friendly data 5. Unsupervised Risk AI adoption is constrained when mistakes could have high costs. Domains requiring deep expertise and zero tolerance for error will resist unsupervised automation. 🟩 No harm if AI gets it wrong 🟨 Low-risk automation is possible 🟧 Medium risk; caution required 🟥 High-risk domain; AI requires constant supervision 6. Interactivity and Integration AI thrives when tools can access real-time context and act on it. AI's effectiveness is sharply limited if software lacks an API or action interface 🟥 No API or integration path 🟧 API available 🟨 MCP-like access for reading 🟩 Full read/write agentic integration Let's score some products we use....

What truly is a disruptive technology? We throw around the term freely these days to refer to any novel technology that we come across. But not all new technologies actually meet the bar. Michael Porter reminds us that a disruptive technology is one that invalidates important competitive advantages of incumbents, enabling a new entrant to leap ahead in the market. Take the Internet for example. It was disruptive where the mechanism for delivering information was fundamental to the product or service. Like travel agents or the recorded music business. But for many other markets, it wasn't disruptive at all, since it was easy for existing incumbents to simply add it as a new channel for communicating with their customers. As GenAI and LLMs become the latest technology innovation, we should ask ourselves, where will it actually be disruptive? Certainly not a chat bot that any incumbent can easily add on. But maybe in the world of search, where LLMs are finally able to deliver a superior answer, potentially disrupting the ironclad superiority Google has maintained for decades. To decide whether something is disruptive, two questions are helpful: 1) To what extent does the new technology invalidate traditional competitive advantages? 2) To what extent can incumbents embrace the technology without major negative consequences for their business? I have no doubt AI will be a disruptive technology, but think we are only in the first inning of discovering exactly where that disruption will occur.

My first startup made t-shirts with a cartoon of a stealth fighter jet. Stealth mode. Fly quietly, under the radar. Catch the incumbents off guard. A familiar refrain. Steve Blank released an article this week entitled: Blind to Disruption: The CEOs who Missed the Future (link in comments). He cites well known “blind to disruption” case studies and then chronicles the failure of entire industry: carriage and wagon manufactures. As the internal combustion engine was emerging, he cited several reasons for their failure: • Technological Discontinuity. Carriages were made of wood; cars, of steel. The skills didn’t transfer easily. • Capital. Retooling for cars required huge investment; most small and midsize carriage firms didn't have it. • Business Model Inertia. Carriage makers sold low-volume, high-margin products. The car business, was about high-volume, low-margin at scale. • Cultural Identity. Carriage builders saw themselves as artisans. Noisy, dirty cars were beneath them. In sum, disrupted CEOs (and in particular, hired CEOs rather than founders), he argues, were blind to the disruption they could not see. Yes, but I think that misses the mark by 1/2. Clayton Christiansen, the (late) “father” of disruptive innovation theory, believed instead that disruptive innovation happens in plain sight. The Innovator’s Dilemma (which describes the perspective of the incumbent) observes that despite being well aware of potentially disruptive technology, the incumbent cannot appropriately respond. Why? • Because new disruption usually starts at the bottom of the market. Disrupters begin by targeting overlooked and underserved segments, often for customers who may not need, or be able to afford, the full capabilities of the incumbent’s offerings. The incumbent cannot and will not trade higher value customers for overlooked, underserved customers. • Because early versions of disruptive products are often inferior on traditional performance metrics, but better on price, simplicity or convenience. As a result, disruptive innovations appeal to customers who do not need all of the attributes of the incumbent's current solutions. The incumbent cannot and will not cannibalize its superior offering with an inferior, cheaper one. The disruptive product steadily improves, eventually meeting the needs of an increasingly large share of mainstream customers. Once this happens, it’s typically too late.  That’s when disruption occurs. Disruption doesn’t occur because incumbents don’t see the future coming. It occurs because they are structurally motivated not to respond until it’s often too late. So be bold, disruptors. They may see you, they may not. It doesn't matter. Do your job, and don't be afraid to let the whole world know. And when they do respond, it'll likely be too late.

I’m pleased to announce the publication of 𝗕𝗜𝗠 𝗧𝗵𝗶𝗻𝗸𝗦𝗽𝗮𝗰𝗲 𝗘𝗽𝗶𝘀𝗼𝗱𝗲 𝟮𝟲 discussing the 𝗘𝗳𝗳𝗲𝗰𝘁𝘀 𝗼𝗳 𝗗𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝗼𝗻 𝗼𝗻 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 within organisations. The article presents a new model for understanding and managing disruptions. The model is intended to 𝘨𝘶𝘪𝘥𝘦 𝘰𝘳𝘨𝘢𝘯𝘪𝘴𝘢𝘵𝘪𝘰𝘯𝘴 𝘵𝘩𝘳𝘰𝘶𝘨𝘩 𝘥𝘪𝘴𝘳𝘶𝘱𝘵𝘪𝘷𝘦 𝘦𝘷𝘦𝘯𝘵𝘴 - whether implementing an innovative technical solution or responding to unexpected market volatility - that shift performance away from the baseline. The article clarifies disruptions by 𝘱𝘰𝘭𝘢𝘳𝘪𝘵𝘺 (+ve or -ve), by 𝘰𝘳𝘪𝘨𝘪𝘯 (internal or external), and delivers a simplified four-quadrant 𝗗𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝗼𝗻 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗠𝗮𝘁𝗿𝗶𝘅 supported by real-world examples. It also outlines a method for assessing disruption 𝘭𝘪𝘬𝘦𝘭𝘪𝘩𝘰𝘰𝘥, disruption 𝘱𝘳𝘦𝘱𝘢𝘳𝘦𝘥𝘯𝘦𝘴𝘴 𝘭𝘦𝘷𝘦𝘭𝘴 and provides examples of a workflow to convert 𝘤𝘩𝘢𝘭𝘭𝘦𝘯𝘨𝘦𝘴 - such as implementing BIM or integrating large language models, into 𝘰𝘱𝘱𝘰𝘳𝘵𝘶𝘯𝘪𝘵𝘪𝘦𝘴 for sustained performance gains. The model explains - at a high level - why some implementation efforts stall while others succeed. 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗮𝗿𝘁𝗶𝗰𝗹𝗲 𝗵𝗲𝗿𝗲: https://lnkd.in/gsdJzeaB I welcome your thoughts on the model and how your organisation deals with disruptions. Are all disruptions avoided, ignored, or sometimes embraced as an opportunity for performance improvement? #DigitalTransformation #Disruption #Innovation #StalledImplementation #Performance #BIM #LLM #Blockchain #BIMthinkSpace #Episode26 #BIMeInitiative #ConstructionIndustry