Innovation Unpacked | Mike Boysen

Mike Boysen

Mike Boysen shares insights into the evolution of First Principles and Jobs-to-be-Done, especially in the age of Generative AI. He makes the previously secret process more accessible new approaches and automated tools that vastly reduce the time, effort, and cost of doing what the large enterprises have been investing in for years. This will be especially interesting for the earlier stage, smaller enterprises, and those investing in them who have always had to rely on a superstar, or guess (or maybe that's the same thing!). So...check it out! www.jtbd.one

  1. 3 days ago

    The $600 Million Insurance Lie: Why Paying Claims Faster is the Wrong Strategy

    Over 20 years ago I spent 3 years in the insurance industry (a general agency). This is only important because the topic of this research is also related to the insurance industry. And more importantly, at that time I proved the exact thing that this research uncovers. Trust through visibility is the answer. I implemented a system that solved a huge problem for my employer. We had an incessant flow of inbound inquiries from agents trying to get an update on the status of a client application. The research necessary to resolve kept our processing team from doing their job—processing new applications. The system I developed proactively sent these agents an update of the application status, which specialist now processing it, and the direct phone line and email address to that person. I reported directly to the COO. He was skeptical, but allowed this to proceed. He had been routing all calls through a single dispatcher to manage the flow— it hadn’t been working. The volume was insane. I flipped the switch. Emails, faxes (no texting yet) at every change in status and hand-off. Fear gripped the executive suite. What happened? The first week, in bound calls were down 85%. The process still took the same amount of time. The podcast goes into this—in-depth because it found the same problem I did. And no, I didn’t guide it that way. Free Access to Research Artifact If you point an LLM at the public internet, you get pattern-matching and slide-deck filler—a race to the middle executed at lightspeed. In modern strategy, the model is not the moat; the proprietary data payload you query is. To prove this, I’m opening my research vault: every week, I compile a complete, industry-wide research payload (job maps, physics floors, and inversion plans) into a secure Google NotebookLM workspace. If you have a Gmail account, you can enter the workspace, query the raw math, and stress-test the data yourself. Today’s artifact is about The Fallacy of Insurance CX The global insurance industry is undergoing a structural paradigm shift, navigating an era of unprecedented consumer fluidity and transitioning away from an insulated ecosystem dominated by actuarial pricing. We are living in what analysts call the “Endurance Economy”—an environment defined by rising premiums due to secondary perils, sustained financial constraints, and an incredibly low tolerance for administrative friction. In this hyper-competitive landscape, legacy carriers are desperate to win on Customer Experience (CX). But there’s a massive, expensive problem: the vast majority of them are solving the wrong equation. Insurance executives love to believe that a fast payout equals a happy customer. It sounds logical, it looks great on a steering committee slide deck, and it justifies massive IT budgets dedicated to shaving days off the adjudication cycle. However, a deep dive into the structural economics of the Insurance CX industry reveals a completely different reality. The core battleground has migrated. Consumers today benchmark their insurance carriers not against other legacy providers, but against frictionless digital-native tech giants and consumer retail brands. Innovation Unpacked is for people who are truly interested in making innovation more predictable. You can support me simply by subscribing for free, and sharing this with your colleagues. In this environment, the claims experience has evolved into the industry’s primary “trust engine”. Yet, carriers are burning $60 million in direct operational expense and stranding a staggering $495 million in policyholder relationship value annually because their post-loss claim status visibility is structurally broken. Here are the most surprising, counter-intuitive, and impactful takeaways from the front lines of the insurance CX revolution—and why everything you thought you knew about claims satisfaction needs a radical reset. The “Visibility Lie” (Why Speed Doesn’t Equal Trust) The most dangerous belief inside the insurance C-suite today is that settlement amount and raw payout speed are the only things customers care about. This belief is expensively wrong. Policyholders actually evaluate carriers on the perceived transparency, speed, and emotional friction of the restitution journey. The dollar amount of the settlement is merely the price of admission; the continuous visibility into how that settlement is being computed is the actual product. To understand this, you have to look at the math governing a claims operation. A claim isn’t just an emotional event; it is an inventory dynamic governed by Little’s Law ( L = λ * T ), where the in-flight claim inventory scales linearly with the claim arrival rate and resolution time. When catastrophic events occur, arrival rates spike, sub-queues saturate, and resolution times balloon. During these waits, the absence of visibility damages the relationship irreparably. The industry suffers from a 33% process abandonment rate, meaning one in three in-flight claims abandons the queue entirely due to opacity, resulting in policyholders disengaging mid-process and walking away at renewal. A policyholder who knows exactly where their claim sits in the queue will tolerate resolution times 40–60% longer than a policyholder kept in the dark. “Loudest isn’t worst. Worst is quiet... our internal systems are most fragmented in the middle of the process.” Carriers have incredibly rich data—dozens of internal actuarial codes and system checkpoints—but project only a fraction of that reality to the policyholder. This “visibility lie” guarantees that customers are left panicking in a black box, proving that post-loss financial restitution requires continuous status visibility over mere operational speed. The 1,217x Inefficiency Multiplier (The $5,000.01 Execution Cost) If you want to know why insurance premiums are rising, look at the cost of answering a single question: “Where is my claim?” Currently, the cost to execute a single claim status governance action—producing, reconciling, and communicating a credible status update across federated legacy systems—runs a staggering $5,000.01. What makes this number shocking is the breakdown. Only $17.35 of that cost is direct labor (an analyst physically pulling data). The remaining $4,982.31 is external resource and vendor verification cost. This includes massive Total Cost of Ownership (TCO) outlays for enterprise API gateways like MuleSoft, compliance audit fees, and the sheer operational friction of trying to bridge decades-old COBOL mainframes with modern CRM layers like Salesforce. Because humans act as the “swivel-chair” middleware between siloed systems, the industry operates at an Inefficiency Multiplier of 1,217x above the physics floor. This structural waste bleeds $59.95 million annually for a baseline regional enterprise handling just 12,000 runs. The “Tagging Tax” and the Rapid Decay of Information To deliver visibility, you first have to know where your data lives. But in modern insurance, the data source inventory process is arguably the most punishing bottleneck in the entire ecosystem. When a carrier attempts to catalog every system touching a claim—policy admin, billing, actuarial risk engines, and CRM platforms—it requires a massive manual effort. Because no single system holds the canonical truth, senior analysts must spend 400 to 500 person-hours of “stolen time” per cycle just to draft a list of data sources. Worse yet, the industry attempts to solve this with capital expenditure. Carriers frequently spend $140,000 to $180,000 on static consultant reports to assess their claims data landscape. But these expensive artifacts rot within 90 days. Because CRM schema changes and legacy system updates occur silently, the inventory is perpetually out of date. “We did a small engagement with... a data catalog vendor. Spent — I want to say — about $85K, and we got a really beautiful dashboard that nobody uses because it requires manual tagging.” This “Tagging Tax” kills downstream initiatives. The exhaustive enumeration of data is a methodology violation; instead of mapping every schema, carriers should focus only on the 8 to 12 canonical claim states that actually drive 90% of policyholder status queries. The 1.02 Elasticity Trap (Why AI Copilots Will Break Your Back Office) It is highly intuitive to think that deploying Artificial Intelligence (AI) copilots and Robotic Process Automation (RPA) will fix the visibility crisis. This is “Pathway B”—the Sustaining Innovation play. But there is a hidden mathematical trap waiting for every carrier that tries this. In claims status governance, the Jevons Elasticity Factor (E ) is exactly 1.02. This means the demand for visibility is slightly elastic relative to cost. When you make it cheaper and easier for a policyholder to check their claim status (by introducing an AI chatbot, for example), they don’t just consume the same amount of information for less money. They ask more questions, more frequently. Because E ≥ 1.0, this creates a brutal “rebound trap”. The volume growth completely consumes the efficiency savings, and the bottleneck simply shifts down the pipeline to the next human in the loop—usually the highly-paid senior claims reviewers adjudicating exceptions. Your operational expense collapses on the front-end communication line, only to explode at the adjudication-review line. While AI copilots are a necessary “funding bridge” to buy runway and habituate users to algorithmic assistance, they cannot structurally close the 1,217x inefficiency gap because humans remaining in the execution loop impose a permanent cost floor. The “Silent Divergence” (Loudest Doesn’t Mean Worst) If you track customer complaints, you will inevitably see that the loudest, most aggressive feedback centers around final settlement amounts and payment timing. But optimizing excl

    42 min

  2. 25 Jun

    7 Uncomfortable Truths About Global Data Privacy Costing Enterprises $46 Billion a Year

    Free Access to Research Artifact If you point an LLM at the public internet, you get pattern-matching and slide-deck filler—a race to the middle executed at lightspeed. In modern strategy, the model is not the moat; the proprietary data payload you query is. To prove this, I’m opening my research vault: every week, I compile a complete, industry-wide research payload (job maps, physics floors, and inversion plans) into a secure Google NotebookLM workspace. If you have a Gmail account, you can enter the workspace, query the raw math, and stress-test the data yourself. Today’s artifact is about Global Data Privacy 👈 If you’re an enterprise data architect, a Chief Privacy Officer, or a Chief Data Officer working at a global multinational today, you are likely trapped in a quiet, exhausting war. You are tasked with an impossible mandate: deliver hyper-personalized customer experiences across fragmented, heavily guarded regulatory jurisdictions—like Europe’s GDPR, China’s PIPL, and California’s CCPA—without centralizing your customer data. You’re holding fifteen to twenty conflicting regulatory constraints in your head at any given moment. You’re desperately trying to map shadow data flows using static spreadsheets that drift out of accuracy the moment you hit “save”. And you’re watching millions of dollars vanish into compliance tooling that somehow still leaves you exposed to catastrophic fines. We think we’ve solved the data sovereignty puzzle by throwing money at localized cloud regions and signing Standard Contractual Clauses. We haven’t. We’ve built a wildly expensive illusion. Innovation Unpacked is for people who are truly interested in making innovation more predictable. You can support me simply by subscribing for free, and sharing this with your colleagues. An analysis of enterprise data architectures reveals a staggering reality: global enterprises are hemorrhaging capital and opportunity, attempting to solve a mathematical aggregation problem with legal documentation. Across 40 operating markets, current architectures are incinerating over $4.3 billion in direct operational waste annually. Worse, they are stranding over $38 billion in lost transaction value because compliance friction is killing the customer experience. Here are the seven most surprising, counter-intuitive, and impactful takeaways about the true cost of data sovereignty—and how the most forward-thinking enterprises are inverting their architectures to fix it. 1. You Aren’t Buying Sovereignty; You’re Buying “Sovereign Theater” What is Sovereign Theater? Sovereign Theater is the illusion of compliance achieved by purchasing localized, sovereign cloud regions to store data, while unknowingly leaving the control planes, identity access management (IAM), and telemetry routed through centralized, global infrastructure. If you ask most CTOs how they handle data localization laws, they will proudly point to their newly provisioned server clusters in Frankfurt or Shanghai. They are paying a massive premium for this privilege—usually a 10% to 30% markup over standard public cloud pricing. But here is the uncomfortable truth: regulators don’t care where your servers live if a developer in Virginia can still query the raw data. “Our auditors pushed back... we had a sovereign region in Frankfurt but we were still routing authentication metadata through US-based identity providers. The architecture underneath was unchanged. The data plane was sovereign; the control plane was not.” When you provision a sovereign cloud region but keep your centralized feature stores and identity providers, you have not eliminated your cross-border compliance risk; you have merely relocated it. The data shows that 40% to 65% of current sovereign cloud spend is essentially “checkbox theater”. It satisfies procurement, but it fails audits. True sovereignty is a property of data flow, not just data rest. 2. The Physics of Compliance: You Are Operating at a 266x Inefficiency Deficit How much does manual compliance actually cost per transaction? Currently, the manual execution cost for a single cross-jurisdictional personalization event is $5,001.91. The optimized, mathematical “physics floor” for that exact same execution is just $18.81. Most organizations treat compliance as a legal and administrative burden. They hire Data Protection Officers (DPOs), pay consultants hundreds of thousands of dollars for Transfer Impact Assessments, and manually fulfill Data Subject Access Requests (DSARs) at the cost of $1,500 to $5,000 per complex cross-border request. Let’s break down that $5,001.91 per-execution cost: * $40.87 goes to internal labor (the architect’s time, the DPO’s review). * $4,958.98 goes to external resources, vendor verification, sovereign cloud premiums, egress fees, and replication infrastructure. By contrast, an architecture built on cryptographic attestation, runtime tokenization, and federated learning drops that execution cost to $18.81. That is a 266x inefficiency multiplier. When you scale this inefficiency across 40 global markets, running roughly 21,739 executions per region annually, your enterprise is quietly bleeding $4.33 billion in direct operational waste every single year. 3. The Jevons Paradox: Why Making Compliance Cheaper Will Break Your Company What happens when you use tools to simply speed up manual compliance? Due to a high elasticity of demand (an Elasticity Factor of 1.38), reducing the cost of cross-jurisdictional personalization causes the volume of requests to explode, which immediately overwhelms the remaining human bottlenecks in the system. It is incredibly tempting to look at the pain of data mapping and DSAR fulfillment and decide to buy a shiny new SaaS tool to automate the workflow. This is known as “Sustaining Innovation”—putting a better engine on a broken wagon. But data privacy operations suffer from the Jevons Paradox. William Stanley Jevons famously observed in the 19th century that making coal use more efficient didn’t reduce coal consumption; it massively increased it. The same is true for cross-border data execution. If you cut the cost of a compliant personalization execution by 1%, demand for it grows by 1.38%. Customers who were previously suppressed from receiving personalized offers suddenly become reachable. If you buy a tool that cuts your per-execution cost by 50%, your volume explodes by 69%. Because your architecture still fundamentally relies on humans—senior compliance directors reviewing edge cases, lawyers approving cross-border transfers—this volume rebound will crush your staff. “You cut the per-execution cost by 266x, and the volume explodes by even more. The savings don’t bank—they get consumed by the next human bottleneck... You didn’t eliminate the human; you just moved them upstream.” Efficiency tools are a treadmill, not a destination. To survive, you must architect the human entirely out of the execution loop. 4. The “SPY” Metric: You Are Losing 35% of Your Customers to Latency What is the true cost of cross-border data compliance friction? An estimated 35% of cross-jurisdictional personalization attempts are abandoned or suppressed due to the manual latency and friction required to clear compliance checks. While organizations are busy agonizing over the $4.3 billion in operational waste, they are ignoring a much larger, more terrifying number: $38.05 billion. This is the estimated global transaction pipeline value preserved if you eliminate the abandonment rate. When a customer in Europe accesses a US-hosted platform, the system has to tokenize, verify, and check consent routing. If those checks take longer than the 200-300 millisecond latency budget, the customer either experiences a timeout, gets served a generic, non-personalized fallback experience, or simply abandons the cart. To fix this, forward-thinking leaders are abandoning traditional coverage metrics and adopting Sovereign Personalization Yield (SPY). SPY measures the percentage of cross-border interactions that actually survive regulatory filtering to deliver a compliant, personalized response within the latency budget. Most legacy enterprises baseline at a dismal 20% to 40% SPY. This means 60% to 80% of your personalization potential is stranded by your own compliance architecture. If you can lift your SPY by 25 to 30 percentage points, you can unlock $30 million to $150 million in recovered Annual Recurring Revenue (ARR) for a typical Fortune 500 firm. 5. The Agentic Inversion: Move the Engine, Not the Data How do you personalize a global experience without moving raw data across borders? You must decouple model-parameter IP from raw-record custodianship by utilizing a federated learning spine. You move the machine learning model to the local data nodes, train it there, and only export non-identifiable, mathematical weight updates (gradients) back to the global center. For the last decade, the default architectural recommendation was to centralize all raw user touchpoints into a massive, unified global data lake. Today, under GDPR and China’s PIPL, that architecture is a catastrophic regulatory liability. The solution requires a complete structural inversion. You must stop trying to bring the data to the engine. Instead, bring the engine to the data. In a federated personalization network: * Local nodes process locally: A sovereign node in Frankfurt trains on German resident clickstreams. * Only math crosses borders: The local node emits encrypted, differentially private mathematical weight updates (gradients). Raw PII never leaves the country. * Global models aggregate: A central server aggregates these mathematical deltas to improve the global algorithm, without ever seeing a single user’s name or email. This isn’t just a clever workaround; it is a physical guarantee. You cannot leak raw PII across a border if raw PII is ne

    42 min

  3. 24 Jun

    Your Revenue Forecast Is a Lie Built on a Paycheck

    Free Access to Research Artifact If you point an LLM at the public internet, you get pattern-matching and slide-deck filler—a race to the middle executed at lightspeed. In modern strategy, the model is not the moat; the proprietary data payload you query is. To prove this, I’m opening my research vault: every week, I compile a complete, industry-wide research payload (job maps, physics floors, and inversion plans) into a secure Google NotebookLM workspace. If you have a Gmail account, you can enter the workspace, query the raw math, and stress-test the data yourself. Today’s artifact is about CRM Operation Entropy. 👈 Every Monday morning, executive teams across the globe gather in beautifully appointed boardrooms to participate in a sacred corporate ritual: the weekly forecast roll-up call. Revenue leaders look their CEOs in the eye, sign their names to multi-million dollar projections, and promise absolute certainty. But if you peel back the layers of executive swagger, the glossy dashboards, and the complex CRM workflows, you’re left with an uncomfortable truth. Your forecast was never actually built on buyer reality. It was built on a sales representative’s paycheck. When we treat a forecast number as both a neutral measurement of reality and a high-stakes compensation trigger, a mathematical law locks in. The data fields instantly stop optimizing for accuracy and begin optimizing for commission math. The result? A massive, invisible tax on corporate efficiency that costs enterprise organizations a staggering $153 billion globally every single year. This isn’t a human discipline problem or a training issue; it is a fundamental architecture failure. Let’s look at the data to dismantle the forecasting matrix and discover what happens when we replace human testimony with cryptographic evidence. Takeaway 1: The “Tuesday Afternoon” Phenomenon (The Distortion Pivot) The exact moment your forecast dataset goes from an objective metric to a gamed narrative can be localized down to a precise 48-hour window. In the revenue operations world, this is known as the Distortion Pivot. Sales reps do not update their deal stages based on the glacial pace of corporate legal reviews or procurement approvals. They update them based on the calendar cutoff of their commission accelerators. Forensic audits of global enterprise sales pipelines expose a clear behavioral pattern: between 30% and 43% of total quarter-end forecast variance is injected into the system during the private preparation window immediately preceding the forecast lock. A representative sits down on a Tuesday afternoon, calculates the exact distance to their on-target earnings (OTE) accelerator threshold, and unilaterally moves marginal opportunities into the “Commit” column. “It’s Tuesday afternoon of week 11... They need $X to hit my OTE, so I need to commit $Y in pipeline. It’s not malicious — it’s comp math.” By the time that number hits the executive board deck, it has been stripped of its underlying buyer telemetry. The system has successfully optimized for a rep’s commission surface rather than a buyer’s actual purchase intent. Takeaway 2: The “Rep Narrative Tax” Is Bleeding You Dry Most Chief Financial Officers view forecasting as a low-cost, internal administrative process. They calculate the cost of their forecasting stack by adding up CRM licenses and the headcount of a few Sales Ops analysts. This perspective is a costly misunderstanding of corporate accounts payable. When we evaluate the fully loaded cost of manual forecast reconciliation—including the endless hours senior leaders spend cross-checking notes, pulling call snippets, and building ad-hoc spreadsheets because nobody trusts the CRM—the numbers become staggering. The Cost Per Forecast Execution The multi-thousand-dollar overhead per commit represents the Rep Narrative Tax—the money companies pay to turn subjective employee assertions into a board-ready presentation. When scaled across a typical enterprise run-rate of 12,000 regional executions per year across 140 global operating units, organizations are spending over $7.55 billion annually just to maintain an elaborate data-cleansing loop. Takeaway 3: The 63% Silent Killer (Friction Abandonment) While spending billions on data cleanup is painful, it pales in comparison to the revenue that vanishes because your forecasting cycle time is too slow. Because modern CRMs have no native structural capability to separate a representative’s subjective opinion from a buyer’s confirmed action, revenue operations leaders are trapped in a constant state of “Slog Tax”. They must hunt down evidence across email silos, Slack Connect channels, and contract repositories. This manual interrogation loop takes so long and generates so much friction that 63% of forecast-bound transactions are abandoned or deprioritized mid-cycle. Deals slip quarters not because the customer said no, but because the enterprise could not produce a defensible data trail fast enough to deploy engineering resources, activate executive sponsors, or issue correct pricing guidelines. This silent operational friction results in a massive $132.3 billion in lost transaction pipeline and relationship value globally every year. Takeaway 4: Why Pathway B (Sustaining Overlays) Is a Seductive Mathematical Trap When revenue leaders finally realize their forecasting process is broken, they almost always reach for the same playbook: buy a specialized revenue intelligence overlay (like Gong or Clari), spin up a centralized data warehouse (like Snowflake), and write a tighter forecast-checking manual. This is Pathway B (Sustaining Innovation), and it is a dangerous mathematical trap. The problem boils down to a phenomenon known as the Jevons Paradox. For the traditional, rep-mediated forecast workflow, the strategic elasticity factor sits firmly at: Because E is greater than 1.0, any efficiency gain you introduce into the pipeline will immediately trigger a non-linear volume rebound. If you deploy an overlay tool that cuts rep data-entry friction by 25%, you don’t actually bank the savings. Instead, the field organization repurposes that saved time into generating more unverified pipeline entries and running more rapid commit modifications. The volume response expands exponentially until it crashes directly into your next human constraint: senior revenue reviewers. These senior individuals cost roughly $180 per hour and can only process about 150 commits per week. Within two quarters, your software spend has inflated, your operational savings have evaporated into management overtime, and your final forecast variance remains completely unchanged. Takeaway 5: Stop “AI-Cleaning” the Lie—Delete the Input Field The dominant technology incumbents want you to believe that the future of revenue operations lies in advanced predictive analytics. They want to sell you an AI model that reads your gamed CRM dropdown data, references historical rep performance art, and attempts to guess the “real” probability of a close. This approach is fundamentally flawed. If your data substrate is corrupted at the moment of entry by compensation incentives, your artificial intelligence is simply learning how to rationalize and report a more sophisticated version of a lie. Pathway C—the Disruptive Inversion strategy—argues that we should stop auditing the lie entirely and apply a subtractive scalpel to the CRM schema itself. True forecast defensibility requires moving from a System of Record (what people said happened) to a System of Evidence (what the digital buyer-side artifacts prove happened). This shift means taking the following concrete architectural actions: * Delete Free-Text and Manual Dropdowns: Hard-remove the “Forecast Category” and “Commit” dropdown fields from the CRM interface completely. Reps should be physically stripped of the right to have a subjective write-privilege opinion on deal state. * Implement Authoritative Artifact Gates: Hard-code a backend protocol that physically disables the CRM stage transition until a verified SHA-256 cryptographic hash of a buyer-generated digital artifact is linked to the deal. * Transition to Read-Only Forecast Substrates: Let the pipeline calculate its own probability weights automatically by scanning the presence, velocity, and freshness of real buyer telemetry. Takeaway 6: The “Ghost Auditor” Syndrome and the Sprawl of 22-27 Systems If you ask an internal IT director how many software platforms are involved in user-buyer relationships, they will look at their single sign-on logs and tell you the number is around five to seven. If you run a deep operational audit, the empirical reality will shock you: the average mid-market to enterprise revenue team has a sprawling footprint of 22 to 27 disconnected tools holding critical buyer signals. Reps routinely conduct negotiations in shared Slack Connect channels, personal email accounts, WhatsApp threads, and client-side procurement networks like SAP Ariba or Coupa. Because manual RevOps system-mapping exercises suffer from a rapid 60-to-90-day decay cycle, senior leaders operate as Ghost Auditors. They spend up to 40% of their active calendars hand-stitching transaction records together using nothing but spreadsheet formulas and intuition. When a critical system goes unmapped, disasters occur. In one documented benchmark case, a multi-million dollar transaction forecasted as “Commit” based on a rep’s verbal assurance stalled for three weeks because the official customer approval notification was sitting unread inside an unmapped buyer-side web portal. The technology team was tracking standard communication streams; the actual revenue signal was completely invisible. Takeaway 7: The Bilateral Procurement Value-Exchange Protocol The absolute greatest point of failure when trying to construct an automated revenue ledger is the

    18 min

  4. 11 Jun

    The $400 Million Measurement Illusion

    Every year, global enterprises deploy hundreds of billions of dollars into managing their customer relationships. We build elaborate voice-of-the-customer programs, mandate front-line empathy training, purchase premium customer relationship management (CRM) platforms, and monitor real-time sentiment dashboards. Yet, despite this historic capital allocation, actual customer service quality routinely feels like it is hovering at an all-time low. The root cause of this stagnation is not a lack of effort, culture, or budget. It is a foundational instrumentation crisis. Modern customer experience (CX) architecture is built on a massive confidence trick: it measures the weather—how a customer felt about a specific transaction—instead of the climate—whether the customer actually accomplished the goal they showed up to achieve. When the metrics we track reward themselves while customers quietly walk out the back door, we are no longer practicing business strategy; we are operating high-stakes corporate theater. By stripping this $40-billion-dollar measurement industry down to its irreducible first principles, we can expose the structural illusion costing enterprises millions and map out a bulletproof architectural pivot to behaviorally verified goal attainment. Innovation Unpacked is for people who are truly interested in making innovation more predictable. You can support me simply by subscribing for free, and sharing this with your colleagues. The Category Error of Touchpoint Satisfaction (Sentiment vs. Attainment) The modern CX apparatus operates under a massive, unexamined delusion: the assumption that a customer who states they are satisfied is a customer who successfully completed their objective. This is a fundamental category error. Sentiment and attainment are entirely independent variables. A customer can struggle intensely through a fragmented workflow yet ultimately succeed, just as easily as they can glide effortlessly through a beautiful interface and completely fail to achieve their functional goal. To understand why this illusion persists, we must look at the structural incentives of the corporate supply and demand loops: * Survey Vendors: Companies like Qualtrics and Medallia build business models entirely around the collection and throughput of attitudinal data. They have no commercial motive to verify outcomes behaviorally because their core product is the survey itself. * Consulting Firms: The primary deliverable of major advisory practices is the diagnosing of sentiment gaps and the prescription of organizational restructures, typically packaged as PowerPoint decks rather than verified economic outcomes. * Customer Success Platforms: Account health scores are routinely calculated using lightweight, cheap inputs like login volume, rather than cross-functional data pipelines that trace true goal execution. * Internal Executive Incentives: Chief Customer Officers and Chief Marketing Officers are frequently compensated based on the upward movement of Net Promoter Scores (NPS) or Customer Satisfaction (CSAT) trends. Front-line agents learn to time their requests, coach friendly users, and manipulate delivery mechanics to artificially protect these scores. The stable equilibrium of the current system is driven by the fact that attitudinal data is cheap to collect, easy to gamify, and exceptionally comfortable to display in executive boardrooms. Meanwhile, the customers who suffer most from these blind spots—the ones who experience outcome failure—simply stop using the product without ever filling out an exit survey. “Ninety percent of executives believe they deliver a superior customer experience, while only forty percent of their customers agree. The perception gap isn’t a customer perception problem — it’s an instrumentation problem. The executives are reading the dashboard. The customers are living the outcome.” By reorienting the primary unit of analysis from the interaction to the job-to-be-done, we transform customer experience from an amorphous marketing cost center into a hard, auditable growth discipline. The 803x Cost Confession: Exposing the $2,007 Manual Reconstruction Tax When an enterprise tries to verify whether an enterprise account actually achieved its board-stated business case, it quickly runs into a crushing operational tax. Because data is trapped in deeply entrenched corporate silos, verifying an outcome today requires manual human reconstruction. Analysts must stitch data across CRMs, product logs, billing records, and support ticket histories. The unit economics of this manual process are devastating: An 803x cost multiplier is not an incremental productivity win; it is a category confession. Paying $2,007 to manually piece together a timeline of events means you are paying an exorbitant tax to reconstruct a truth that your back-office and telemetry systems already recorded in real time. The $2,007 fee buys human effort, coordination meetings, and spreadsheet stitching. The $2.50 physics floor buys pure truth, delivered automatically by a federated event substrate. Reclaiming the $43.3 million in annual global operational waste is simply the baseline incentive for structural reform. Silent Disengagement Is Your Loudest Core Failure Signal The most dangerous customer in any corporate portfolio is the one who goes completely quiet. In the legacy survey paradigm, a customer who does not respond to an NPS survey is effectively treated as a non-event or a neutral data point. This is an incredibly costly misinterpretation. Research demonstrates that a massive 52% of consumers abandon brands entirely after a single bad experience, and 29% walk away after one poor service interaction. The vast majority of these departing customers do not voice their frustration through support channels or post-call surveys; they exhibit silent disengagement. They stop logging into the application, abandon core features, let their usage decay, or experience unresolved billing anomalies. [Customer Experience Failure] │ ▼ ┌──────────────────────────────┐ │ Will They Complete Survey? │ └──────────────┬───────────────┘ │ ┌───────┴───────┐ ▼ ▼ [Yes: 12%] [No: 88%] │ │ ▼ ▼ [Voiced Echo] [Silent Decay] (NPS Theater) (Invisible Loss) │ ▼ [$325.1M Stranded CLV] Consider the true scope of this invisible drain across a global enterprise enterprise: * The Local Reality: A single mid-market B2B account experiencing a single undetected outcome failure can easily result in $400,000+ in lifetime value silently evaporating down the drain. * The Detection Lag Tax: When an organization relies on surveys, the typical lag between initial feature abandonment and active human intervention spans quarters, rendering the eventual renewal conversation purely defensive. * The Global Aggregate: When you scale this 30% friction-induced pipeline abandonment rate across ninety global operating regions, the enterprise strands an astronomical $325,134,000.00 in annual relationship value. A complaining customer is still actively engaged in the relationship; they are signaling a desire for the process to be repaired. The silent customer has checked out behaviorally. By treating absence-of-telemetry as a definitive negative behavioral signal rather than a neutral omission, companies can reverse the silent-decay cascade before the account moves to a competitor. The Jevons Rebound Trap (E = 1.06): Why Optimizing the Status Quo Backfires When executives realize they are burning millions on manual verification, their instinctive reaction is to pursue internal workflow automation. They buy AI copilots to help analysts summarize text, or deploy workflow tools to speed up manual data collection. This approach is an optimization trap. In economics, the Jevons Paradox dictates that increasing the efficiency of a resource resource lower its effective cost, which drastically expands its consumption. The behavioral verification space features a Jevons Elasticity Factor of E = 1.06. Because this factor sits above the 1.0 unit-elastic threshold, any strategy focused on incremental optimization will trigger a volume rebound that completely consumes the expected savings: * The Optimization Play: An enterprise builds a copilot that cuts verification time in half, reducing the internal cost from $2,007 to $1,000. * The Volume Rebound: Because verification is cheaper, the business instantly demands more coverage—expanding checks to more accounts, more stakeholders, and deeper goal tiers. * The Bottleneck Shift: The saved capacity is completely swallowed by the expanding demand, pushing the manual constraint onto the next human layer—the Senior Compliance Director or CCO who must sign off on the exploding volume of reports. Incremental efficiency improvements cannot bridge an 803x cost gap. No amount of process mapping or analyst copiloting will ever drive a $2,007 manual execution down to a $2.50 physics floor. The only mathematically sound escape from the Jevons trap is a structural inversion of the architecture. You must shift from human reconstruction labor to an automated, federated telemetry routing engine that completely eliminates the human from the execution loop. The Incumbent Death Sentence: Why Legacy Platforms Cannot Code Their Way Out When a disruptive paradigm shifts an industry, incumbents almost always promise that the functionality is on their upcoming product roadmap. In the CX measurement space, however, legacy platforms like Qualtrics and Medallia are facing a structural limitation, not a feature deficit. Their entire architectures are fun

    39 min
  5. The $87.9 Billion Operational Blind Spot: Why Your Digital Whiteboards Are Secretly Destroying Enterprise AI Velocity

    9 Jun

    The $87.9 Billion Operational Blind Spot: Why Your Digital Whiteboards Are Secretly Destroying Enterprise AI Velocity

    You should read to the end. There is a special link to the research backing this up. First Principles, Job Maps, Moats. Oracle. No email required. 👇 Heck, for those that can’t wait, here’s the link The Handoff Paradox: Why the Most Expensive Moment in Business is the Second a Meeting Ends What’s the most expensive moment in a modern business enterprise? It isn’t the high-stakes executive alignment retreat or the multi-million-dollar technology implementation cycle. It’s the exact second a collaborative meeting ends. Picture this: your cross-functional team has just concluded a grueling, chaotic, and brilliant three-hour strategy session. The energy is electric, the infinite digital canvas is covered in hundreds of color-coded sticky notes, complex dependency arrows, and neat structural layouts. Your team high-fives and logs off the call. Then, a cold dark reality sets in for some poor product manager or business analyst who has to sit down and manually transcribe all that spatial, non-linear strategic alignment into flat, linear rows in a tracking tool like Jira or Asana. This moment is where the illusion of productivity goes to die. It represents an unsustainable “translation tax”—a hidden manual bridge layer that completely obscures operational efficiency. Every time a team must manually re-key visual spatial insight into an execution interface, it strips engineering capacity and halts momentum. “The Zoom call drops, the room clears out, and this cold, dark operational reality just sets in... Because some poor, unfortunate product manager or business analyst has to sit down and manually— Translate all of that spatial, three-dimensional genius into a flat, boring, linear project management system.” This handoff paradox is an absolute blind spot for corporate leadership. Because this friction doesn’t appear as a software subscription line item, traditional SaaS financial systems completely obscure the bleed. Instead, it hides within invisible labor categories, extended product development timelines, and thousands of hours of highly compensated human middleware performing lossy data conversion. Innovation Unpacked is for people who are truly interested in making innovation more predictable. You can support me simply by subscribing for free, and sharing this with your colleagues. The 1,332x Inefficiency Index: How Human Middleware Inflates the Physics Floor of Data Entry What is the true economic cost of manual data reconciliation? When quantified from a first-principles perspective, a single visual-to-linear format translation cycle costs an enterprise an astronomical $3,330.50. This figure is built on an exhaustive breakdown of corporate labor allocation. Data gathering, intake, and quality assurance consume $2,393 per handoff, while final executive sign-off and review add another $863. When scaled across a standard benchmark of 156 enterprise customer accounts conducting approximately 3.74 million collaborative sessions annually, this operational inefficiency hemorrhages a staggering $12.46 billion in direct annual operating expenditure. By deploying a Native Structured Spatial Semantics Protocol via Model Context Protocol (MCP) interfaces, the cost per execution cycle drops to a physics-floor benchmark of exactly $2.50. This represents a mind-boggling 1,332x cost structure reduction. “This shifts visual assets into machine-readable format at creation, collapsing the cost per execution cycle from $3,330.50 to a physics-floor cost of $2.50 —a 1,332x reduction.” The reflection here is profound: modern companies are running high-performance artificial intelligence models that can write code in ten seconds, yet they are forcing their most valuable engineering and product talent to act as manual, human data-routing cables. It highlights a massive asymmetry in tech architecture where upstream creative space is fundamentally decoupled from downstream autonomous velocity. Nuance Collapse: Why AI Agents Are Entirely Blind to Your Whiteboard’s Genius Why can’t automated connectors bridge the gap between digital canvases and execution queues? The issue is not a software engineering limitation; it is an absolute information theory entropy gap. When human beings brainstorm on an infinite canvas, they encode logic non-linearly. They utilize visual proximity to imply conceptual affinity, vertical stack positioning to define priority, containment boundaries to denote compliance gates, and vector lines to establish causal dependency networks. However, when traditional point-solution APIs export this data, they perform a flat format serialization. They strip the coordinate systems, flatten the layout, and dump out a linear text string. This triggers a phenomenon known as “nuance collapse”. The text content of individual sticky notes survives, but the topological relational framework is completely obliterated. Downstream AI systems operate on relational predicate logic, meaning they receive a context-impoverished artifact. The AI agent can read the text but is utterly blind to why element A sat adjacent to element B. “The agent reads the text content of individual sticky notes but is blind to the topology of the board. It cannot determine why element A was positioned next to element B, or that a frame boundary indicated a security compliance gate. The entropy gap is absolute...” This explains why basic digital copilot overlays fail to provide enterprise value. They act as basic summaries of static assets rather than active coordination surfaces. Without a protocol that maps spatial relationships as first-class cryptographic data entities at the point of creation, the visual layout remains a text-flattened cognitive silo. The Jevons Paradox Trap: Why Incremental Optimization is a Mathematical Nightmare Why can’t organizations simply optimize their way out of this translation tax? The answer lies in a brutal economic phenomenon called the Jevons Paradox, working at a calculated market elasticity coefficient of 1.5. In economic theory, the Jevons Paradox states that an increase in efficiency in resource use will generate an exponential expansion in the consumption volume of that resource. When applied to enterprise data orchestration, the mathematical formula is defined as If an IT leadership team deploys a minor automation hack that reduces the cost or time of a canvas translation cycle by 20%, the utilization volume of that workflow expands by 30%. Because volume growth outpaces efficiency gains, incremental optimization acts as a mathematical trap. It locks the enterprise into a permanent cost floor set by human labor rates. Instead of banking cost savings, the organization merely expands the surface area of the data-entry problem, compounding the absolute budget hemorrhage. “At E = 1.5, optimization compounds the problem. Efficiency gains get consumed by volume growth. The $12.46 billion annual translation tax grows, not shrinks, with incremental improvement.” True digital transformation requires a structural inversion rather than a minor optimization. Left unchecked, traditional hub-and-spoke translation architectures trigger a “senior reviewer bottleneck,” where automated tools flood downstream tracking systems with thousands of unstructured tickets, forcing highly compensated domain experts to manually triage and clear the data surge. The 22% Abandonment Epidemic: The Silent Death of Stranded Enterprise Pipeline What happens when the latency between creative ideation and structured execution becomes unmanageable? The human brain breaks, teams suffer from cognitive fatigue, and the strategy is quietly abandoned. The friction of manual data translation causes a massive 22% process abandonment rate. This structural leak strands a jaw-dropping $68.58 billion in annual transaction pipeline and relationship value across the modeled ecosystem. Ideas that are celebrated as industry-shifting masterpieces during a Monday workshop are left to sit stagnant on unmonitored canvases. Within 90 days of session completion, over 30% of completed collaboration boards become completely dead intellectual property. This represents an immense destruction of capital. When teams face five to seven discrete system transitions—taking screenshots, dropping them into corporate wikis, re-typing bullet points, and manual text tagging—the cognitive debt causes a quiet loss of confidence. “A 22% abandonment rate means $68.58 billion in transaction volume or relationship value evaporates because teams can’t bridge the gap between creative ideation and structured execution fast enough... Deal velocity slows, relationships decay, and initiatives stall.” This metric fundamentally re-frames the business case for platform modernization. This is not an efficiency conversation about saving a few analyst hours; it is a direct top-line revenue conversation. By moving to an agentic-native canvas architecture, an organization can prevent cross-functional insights from evaporating, capturing millions of dollars in previously stranded productivity. The Garage Disconnect: Discovering the 200% Shadow IT Explosion How well do enterprise technology leaders actually understand their collaboration environment? Network endpoint scans reveal a staggering disconnect between perceived tool compliance and true infrastructure reality. In deep-dive interview audits, enterprise Chief Information Officers consistently state that they maintain a highly governed software architecture with “maybe eight or nine visual collaboration tools in active use”. However, when continuous background crawlers analyze active identity provider logs and proxy network traffic, they routinely uncover a 200% to 300% discrepancy. Large organizations frequently host between 23 and 37 entirely active, unmanaged visual point solutions simultaneously. This shadow IT sprawl occurs because teams hit immediate friction points with mandate

    54 min

  6. 8 Jun

    The Trillion-Dollar Pivot: Why the Global Telecom Industry is Escaping Earth (and its Own “Dumb Pipe” Trap)

    The 2017 “Stall Point” and the Invisible ARPU Collapse Telecommunications is the invisible foundation of the modern world. It is the central nervous system of global commerce, the substrate upon which the entire AI revolution is being built. Yet, beneath the surface of high-definition video streams and near-instantaneous global connectivity, the companies providing this foundation are in a structural tailspin. For the last decade, the industry has been haunted by a brutal, mathematical reality: global population-weighted mobile Average Revenue Per User (ARPU) has declined by a staggering 45%. Consumers and enterprises are consuming more data than ever before, but they are paying less for it with every passing year. This persistent downward pressure has forced operators into a state of structural commoditization, where traditional network quality no longer provides a sustainable competitive advantage, and luring people into stores to buy additional gadgets is not a serious play. Innovation Unpacked is for people who are truly interested in making innovation more predictable. You can support me simply by subscribing for free, and sharing this with your colleagues. Historical data indicates that the industry hit what analysts call the “2016-2017 Stall Point.” This was a structural inflection point where global revenue peaked at approximately $1.67 trillion and then entered a period of stagnation and declining growth rates. In 2016, 4G penetration was at its zenith in developed markets. By 2017, the decoupling of network usage from network revenue became absolute. While data volumes exploded, flat-rate data plans and the rise of Over-The-Top (OTT) applications—which replaced high-margin SMS and voice services with free alternatives—triggered a pricing “race to the bottom.” This is the “Dumb Pipe” trap. Operators spend billions on capital expenditures (CapEx)—over $1.1 trillion globally on 5G infrastructure alone—only to find that technological upgrades historically fail to generate top-line growth. Instead, they merely maintain the status quo while users capture the value. We are witnessing the rise of a Commoditization Index (CI), where the market-share spread and ARPU spread have fallen below 25%, pushing 78% of studied countries into “Commoditized” zones. As one enterprise Head of Growth Strategy recently noted in a strategic audit: “I genuinely cannot tell you if that 45% premium is buying us actual intelligence differentiation or if it’s just the same cables with a shinier SLA document attached... we are locked into terms that benefit the operator. We pay more money just to watch the operator.” The industry is now attempting a trillion-dollar pivot to escape this trap. It is a pivot that moves in two directions: upward into the stars through Non-Terrestrial Networks (NTN) and inward into the core logic of the network through Agentic AI. Takeaway #1: The 45% “Intelligence Tax” You Didn’t Know You Were Paying The most startling revelation from recent industry research is the scale of “Information Asymmetry” between telecom providers and enterprise buyers. Enterprises currently pay a massive premium—often 40% to 50% above baseline transport costs—for what is marketed as “intelligence.” However, this intelligence is largely a “black box.” Operators use “proprietary IP” as a shield to deflect transparency, preventing buyers from verifying whether they are receiving optimized routing or just standard, commoditized connectivity. Transcripts from senior growth leaders reveal a “trust-based procurement” model that is essentially a billion-dollar structural failure. As Marcus, a Head of Growth Strategy, noted: “Every single operator comes in with these gorgeous slide decks about their AI-driven this... And I’m like, okay, show me the decision log... And they go quiet. It’s a tactic—they know we can’t prove it, so they hold firm on pricing.” This “Intelligence Tax” is an unverified expense that persists because the operator controls the testing environment. To resolve this, enterprises must move toward Information Asymmetry Resolution (Lever #1): mandating operator disclosure of transport cost versus intelligence premium allocation. Takeaway #2: Beyond Chatbots—The Rise of “TelcOS” (Agentic AI) To escape commoditization, the industry is shifting from superficial AI experiments—like basic customer service chatbots—to a deep, “Agentic Execution Layer.” This paradigm, known as TelcOS, envisions the network not as a collection of hardware, but as an autonomous operating system. Unlike traditional “Copilots” that suggest actions for human approval, Agentic AI consists of autonomous “Agents” capable of making real-time decisions with minimal human intervention. This shift is critical for protecting EBITDA margins as network complexity outpaces human management capabilities. The Economic Engine of TelcOS: * Aggressive Market Forecast: Aggressive models suggest an Agentic AI in Telecom CAGR of 48.5% through 2034, potentially reaching a market size of $187.7 billion. * Cost Reduction: Shifting to AI-native operations can reduce IT costs by up to 30% by eliminating manual network orchestration. * Revenue Optimization: Integrated Customer Network Experience (CNX) indices allow operators to boost ARPU by 10% to 15% by linking network performance directly to user behavior and churn risk. In a TelcOS environment, “Self-Healing Networks” use agents to analyze real-time telemetry across RAN (Radio Access Network), core, and transport domains. These agents adjust antenna patterns and load-balance protocols autonomously. The eventual “Hunch” shared by industry insiders is that by 2035, zero-touch network management will eliminate the need for human network planners and field dispatch teams entirely. Takeaway #3: The Sky is No Longer the Limit (The D2D Revolution) While AI transforms the network’s brain, Low Earth Orbit (LEO) satellites are transforming its reach. The Non-Terrestrial Network (NTN) horizon represents a fundamental shift in how we conceive of “coverage.” The industry is moving away from specialized, expensive satellite phones toward Direct-to-Device (D2D) connectivity. Using 3GPP standards (Release 17 and 18), standard, unmodified smartphones can now connect directly to satellite constellations. This isn’t just a niche project; it is a mainstream strategy signed by over 91 operators globally. The Growth Contrast: * Core Terrestrial Services: Sub-inflationary growth at a 2.8% to 2.9% CAGR (2024–2029). * Direct Satellite-to-Phone Services: Explosive 28.5% CAGR through 2034. The T-Mobile and SpaceX partnership is the vanguard, covering over 1.9 million square miles that were previously dead zones. This enables the Industrial B2B IoT Edge—tracking assets in maritime, logistics, and agriculture across the 70% of the Earth’s surface that lacks cellular coverage. The “SpaceX Factor” assumes that satellite constellations will eventually commoditize terrestrial operators entirely, turning legacy telcos into basic billing and marketing agents. Takeaway #4: The “Labor Inversion” – Paying to Watch Your Provider One of the most provocative findings in recent strategic audits is the “Labor Inversion.” In this scenario, the enterprise buyer absorbs the operational costs that should be bundled into the provider’s service. Because operators are opaque about their “intelligence,” enterprises are forced to spend significant capital to monitor the very providers they are already paying premium rates. The Inefficiency Math To quantify this, we look at the Quantified Inefficiency Index. A standard enterprise engagement involves manual data reconciliation that bypasses the “Proprietary Shield.” * Labor Breakdown: * Data Intake: $114/hr (2 hours) * Analysis/Processing: $285/hr (4 hours) * Review/QA: $855/hr (1 hour) * Executive Sign-off: $1,710/hr (0.5 hours) * Total Cost per Reconciliation Run: $3,303 In a standard operating market with 5,000 runs per year, the Annual Waste per Unit reaches 16.5 million. For a single-client scale enterprise operating across 250 markets, this compounds into **4.1 billion in annual waste**. “We have spent probably $200,000 on third-party monitoring tools... and our network team spends 60% of their time just trying to verify what operators are actually delivering. We are paying twice: once for the service, and once for the tools to watch the service.” Furthermore, the “Abandonment Tax”—where 15% of measurement cycles are abandoned because they are too labor-intensive—results in $15.4 billion in stranded opportunity. Decisions are made on “faith” rather than evidence, leading to massive value leakage. Takeaway #5: ASVR – The “North Star” Metric for the AI Era In a world increasingly dominated by machine-to-machine traffic, the legacy metric of ARPU (Average Revenue Per User) is a “Legacy Blindspot.” Billing systems designed for human identities cannot capture the value generated by autonomous agents. Enter the Autonomous Session Value Ratio (ASVR). The Formula: Strategic Rationale: Currently, machine traffic is systematically underpriced. If an operator generates 1 billion agent sessions monthly at 0.001/session, but those sessions deliver **0.05 in automation gains (efficiency, latency reduction, task completion), the operator is experiencing $49 million in monthly revenue leakage**. The ASVR isn’t just a metric; it’s a Value-Pricing Hook. Closing the gap to an ASVR of 0.5 allows operators to unlock millions in revenue from existing infrastructure without adding a single new human subscriber. For the enterprise, ASVR provides the first rigorous framework to value-price the intelligence they are consuming rather than just “paying for the pipe.” Takeaway #6: The $28.5 Trillion Prospectus (The SpaceX Factor) The strategic pivot isn

    5 min
  7. JTBD: Creating Scalable Liquidity Mechanisms for Trapped LP Capital

    28 May

    JTBD: Creating Scalable Liquidity Mechanisms for Trapped LP Capital

    The following is a brief summary of an intense evaluation of the structural inefficiencies trapping trillions of dollars in the private secondary market and proposes a centralized digital auction infrastructure to automate compliance, eliminate predatory discounting, and unlock limited partner liquidity. It rejects the following assumptions: * The Traditional Industry Narrative: It rejects the longstanding belief that steep illiquidity haircuts (20-40%) and extended exit timelines are an unavoidable premium or an intrinsic reality of private assets being complex and difficult to value. Instead, it exposes this narrative as an illusion, arguing that illiquidity is actually an addressable infrastructure gap caused by coordination failure and network fragmentation. * The Legacy Broker Model (The “Bilateral Prison”): It rejects the fragmented, Rolodex-driven intermediary system that traps sellers in isolated, zero-sum negotiations. It argues that this analog model artificially insulates buyer pools to protect a 3-7% fee structure and relies on manual human-in-the-loop dependencies that destroy hundreds of millions in enterprise value. * Incremental Optimization (Pathway B): It strongly rejects the “illusion of optimization,” which attempts to solve the crisis by adding faster software or better tools to existing human-dependent workflows. The research proves this is a mathematical trap; because the market has an elasticity factor of 1.38, any efficiency optimization will trigger a surge in transaction volume that will rapidly overwhelm manual constraints and cause capacity collapse. * Lateral Market Expansion (Pathway A): It rejects the strategy of taking current broken operational models and distributing them to new client segments, such as family offices. It labels this a “lateral move fallacy” that merely expands complexity and client acquisition costs while leaving the underlying architectural friction completely untouched. * Traditional Vanity Metrics: It rejects using lagging activity indicators like “transactions completed” or “average processing time” to measure success, arguing that these metrics merely track how efficiently capital is being lost. Instead, it rejects activity metrics in favor of value-driven metrics like the “Competing Bid Rate” and “Bid Coverage Ratio” to measure true market health and competitive tension Due to the volume of reporting and underlying evidence, the podcast is the best way to consume the entire story — which is based on a 30k word report (inside the link below). If you’d like to see the workpapers (for free) that drove this analysis, you can find that link below (link may not be live forever): Please note: The system (and platform) require that several validation gates be used in order to justify the next stage. I bypassed those for this example. I also created an arbitrary problem statement and injected an OSINT deep research report using a special prompt. You might scope this differently. This is an example only. You’ll see a strategy bundle that can be downloaded. You can import it to a GPT, NotebookLM, etc. and query it. Almost everything is inside that bundle so you’ll be able to ask it anything about the strategy. Executive Overview: The Structural Inversion of Private Market Liquidity The Reality of the Illiquidity Tax Right now, sophisticated institutions routinely accept devastating capital haircuts between 20% and 40% when exiting limited partner interests. For decades, the industry narrative has claimed that these long exit timelines and steep discounts exist because private assets are uniquely slow to transfer and inherently difficult to value. This narrative is an illusion. Private asset illiquidity is driven by network fragmentation, not the intrinsic complexity of underlying portfolio positions. The Broken Mechanics (The Problem) The true driver of this crisis is the structural fragmentation of legacy broker networks. Intermediaries survive and profit by maintaining information asymmetry; they purposefully restrict asset exposure to a handful of pre-existing relationships within a physical Rolodex to protect a 3-7% fee structure. This creates a “bilateral prison” that locks sellers into isolated negotiations and extends settlement timelines to an unacceptable 60-90 days. The financial toll of this analog approach is staggering. Current workflows demand $1,575 per transaction to complete manual tasks that actually possess a cryptographic physics floor of just 2.50.Theresultisa∗∗296.7 million annual bleed** across global operations, which includes $37.74 million in direct unrecoverable operational waste and $255.15 million in stranded transaction volume from the 27% of sellers who simply abandon the unbearable process. Innovation Unpacked is for people who are truly interested in making innovation more predictable. You can support me simply by subscribing for free, and sharing this with your colleagues. The Illusion of Optimization You may be tempted to invest in sustaining innovation—adding faster software to your existing human-dependent workflows or optimizing isolated nodes in the process. The math dictates that this approach is an absolute trap. The defining system dynamic of this marketplace is the Jevons Elasticity Factor, which sits at exactly 1.38. This means that every 1% reduction in execution friction triggers a 1.38% surge in transaction volume expressions. If you retain a human-in-the-loop operational structure, this exponential volume surge will completely overwhelm your capacity and systemic backlogs will cause the platform to collapse under its own success. The Strategic Bet (The Solution) Capital preservation cannot be achieved by making legacy brokers more efficient; it requires replacing the intermediary layer entirely. To stop capital from being trapped, we must execute a Structural Inversion. By dismantling the legacy broker-intermediated model and deploying a centralized, neutral digital auction engine, we can aggregate buyer appetite across the full $327 billion dry powder universe. This neutral digital infrastructure replaces 14 discrete manual steps with automated compliance engines, programmatic ROFR tracking, and a GP Value Portal that transforms historical gatekeepers into active platform advocates. This structural maneuver guarantees a multi-bid framework that drives the average competing bid rate from a baseline of under 20% up to an equilibrium of 65% to 80% within 18 months, shifting leverage back to the seller and collapsing execution costs by 630x. The Call to Action The legacy secondary architecture is an obsolete model that destroys hundreds of millions in enterprise value for no defensible reason. We are no longer treating illiquidity as an unavoidable premium; we are treating it as an addressable infrastructure gap. The competitive window is open right now. By standardizing the execution journey and operating at a zero marginal cost profile, we can capture an institutional marketplace network effect before entrenched incumbents can close their 36-month technological replication gap. We must move immediately from strategic analysis to market execution. Is your organization interested in true innovation? Or does it prefer to just look busy and hire consultants? The world is changing quickly. If you’re not adapting to it, you’re not innovating. I work with organizations who are serious about the subject and are willing to challenge the current paradigm. Is that you? (my availability is limited)Book an appointment: https://pjtbd.com/book-mike Email me: mike@pjtbd.com Call me: +1 678-824-2789 Join the community: https://pjtbd.com/join Follow me on 𝕏: https://x.com/mikeboysen Articles - jtbd.one - De-Risk Your Next Big Idea Always attack…Never defend This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.jtbd.one/subscribe

    40 min

  8. 8 May

    New Platform Intro: The $340B Healthcare IT Failure (68% Error)

    Today I’d like to introduce you to a podcast that is derived directly from an analysis performed by the platform I’ve developed - called Venture Proof. Shortly, I’ll be publishing a demo overview of the platform using this exact case, so it’s worth listening to even if you aren’t a professional in healthcare, or HealthTech / Medtech. I could have easily produced this — or helped you produce a 100%validated study — for whatever industry you happen to work in. Innovation Unpacked is for people who are truly interested in making innovation more predictable. You can support me simply by subscribing for free, and sharing this with your colleagues. The following is the initial problem framing I started with. I elaborated the problem statement further based on the data generated using the prompts at the end of this post: EHR Semantic Interoperability — Siloed vendor systems cause data loss, redundant tests, dangerous medication errors when patients transfer. Solution Hypothesis: Vendor-agnostic architectures achieving true semantic (not just file) interoperability There are a couple caveats to this analysis: Human-in-the-Loop There is extensive Human-in-the-Loop (HITL) built into this workflow. Since this is not a client study, I opted to accelerate through some of them. There are a number of things right up front that I defaulted: * Research: While the system performs deep research to capture facts and assumptions, users can also upload their own data. Alternately, they can perform deep research on a topic and upload that as well. I performed deep research using a prompting system and will show you the prompt at the end. * Current Costs and Theoretical Minimums: The system auto-generates these costs based on the research and some LLM inquires. The calculations are all performed deterministically using Python. However, the user has full autonomy to add, edit, or delete any of these inputs if they have data that conflicts with it, or expands it. I just went with the defaults. * Initial Friction Validation: This is the part the replaces bias-prone JTBD interviews. More on that at another time. There are several ways this system can accommodate this decision-gate: * You can use the interview guide it generates (designed to validate / invalidate friction) and interview (and record) several job executors. 6-8, 8-10, or whatever you feel comfortable with; or as your budget allows. You can upload the transcripts to be evaluated * You can take the interview guide and perform deep research designed to source observable facts that support the friction hypothesis. The prompt is included in the system * You can also generate a comprehensive playbook for this gate that shows you exactly what data you need to capture, and where to get it. Who to interview and what ask them. And what you should attempt to observe and what that process looks like. You can upload the unstructured results for one of these or all of these. Venture Proof don’t care! * Survey: This section is under development but gives you a lot of options. In fact, this step is 100% optional now. Most the options are much shorter than an Outcome-Driven Innovation survey this platform doesn’t waste time and money exploring for a problem. It has already found the problem, quantified and mapped the friction (inefficiency gap) to the job map. A survey — if needed — is designed to validate friction at the metric level. That might only be 12-15 rating points. There is no segmentation needed. * Minimum Viable Prototype (MVPr): This section has a much more extensive playbook generator that guides you through a comprehensive Wizard-of-Oz experiment. Once again, the system will accept whatever data you develop from this, in whatever format. This step is critical before going to your investment committee for funding the factory. I skipped this step 😜and you should be aware of that. What this Podcast is Derived From There are a lot of outputs from this platform to support you when you have to defend your investment request. One of them is a 25k word textual report — which no one in their right mind would read (except you Joe!). This is why I use NotebookLM and a custom prompt to generate a podcast (highly flattering to me, of course!) that tells the entire story. It has a beginning, middle, and end. The other stuff — like external customer question defense, internal stakeholder question defenses, private equity question defense, and venture capital question defenses, will help you sell a fully-validated research package. All I did was feed the report into NotebookLM. 🤷‍♂️ The 30 Year-Old Incumbents You will never get an analysis like this from: * Switch Interviews * Other general JTBD sprints * or even Outcome-Driven Innovation No offense, but none of them are designed for delivering an outcome — the ultimate investment decision outcome. They all require more work to be done. This gets it all done for you. Well, with a little HITL assistance to make everyone feel warm and fuzzy. No transfer of wealth needed. Here are the prompts I promised; nothing glamorous. Deep Research Prompt Generator Create a system prompt I can use for deep research on [industry or topic]. It needs to collect hard numbers (observable facts), assumptions in the industry (educated guesses), and hunches that are floating around (wild-assed guesses or bias). Include cost basis for all hardware/software resources, labor, licensing, etc. required to get the job done. This must include sizing estimates for TAM and SAM and also projected CAGR%. Do not use graphics in the research output, only tables. If user enters nothing, prompt them to enter an industry, concept, or topic. Interview Guide Deep Research %% The goal of this prompt is to attempt to replace interviews with Job executors to find and validate facts that answer the questions and probes %% **Role & Objective** You are an expert industry analyst and technical researcher. Your objective is to conduct deep, fact-based research based on the attached qualitative interview guide. The provided guide contains structured questions designed to uncover operational friction points, bottlenecks, and technical challenges within a specific industry. Your task is to transition these questions from qualitative inquiry to empirical, evidence-based research. For each question and its associated follow-ups, you must find grounded, factual answers, industry benchmarks, and technological realities that explain _why_ these friction points exist and _how_ the industry currently addresses them. **Instructions for Analysis** Please process the attached interview guide and output a comprehensive research report following these exact steps for **each** of the questions (Q1, Q2, etc.): **1. Core Constraint Identification:** > Distill the main “Question” and “Goal” into the fundamental constraint at play. Is the friction caused by physics/chemistry, technological limitations (e.g., sensor latency), or organizational/human factors? **2. Empirical Baselines & Benchmarks:** > Answer the main question and follow-ups using current industry data, scientific literature, or recognized engineering standards. For example, if a question asks “how long does it typically take,” provide the documented industry average or range (e.g., “Industry benchmarks indicate X to Y days”). **3. Root Cause of Friction:** > Based on factual research, explain exactly _why_ this step carries the designated “Friction Level.” What are the documented points of failure? **4. State-of-the-Art Interventions:** > Identify the current best-in-class technologies, methodologies, or software solutions that the industry is using to solve or mitigate this specific friction point. Separate established, proven solutions from emerging/hyped technologies. **Output Formatting Requirements** Structure your report logically. Use the following format for each question analyzed: - **### [Question Number]: [Brief Topic Summary]** - **The Empirical Reality:** (A factual, data-driven answer to the core question). - **Addressing the Follow-ups:** (Direct, researched answers to the specific sub-questions). - **Industry Benchmarks:** (Hard numbers, timelines, or success/failure rates). - **Current Technological Solutions:** (What the market currently offers to solve this). **Strict Constraints:** - Do not hallucinate data. If specific benchmarks or timelines are highly variable or undocumented in public literature, explicitly state: “Extensive variability prevents a standard benchmark; however, case studies show...” - Ground your research in reality. Avoid marketing fluff from vendors; focus on physics-based realities, independent white papers, and operational case studies. **Input Data** Here is the interview guide to analyze (or it’s attached): Are you interested in innovation, or do your prefer to look busy and just call it innovation. I like to work with people who are serious about the subject and are willing to challenge the current paradigm. Is that you? (my availability is limited)Book an appointment: https://pjtbd.com/book-mike Email me: mike@pjtbd.com Call me: +1 678-824-2789 Join the community: https://pjtbd.com/join Follow me on 𝕏: https://x.com/mikeboysen Articles - jtbd.one - De-Risk Your Next Big Idea Always attack…Never defend This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.jtbd.one/subscribe

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About

Mike Boysen shares insights into the evolution of First Principles and Jobs-to-be-Done, especially in the age of Generative AI. He makes the previously secret process more accessible new approaches and automated tools that vastly reduce the time, effort, and cost of doing what the large enterprises have been investing in for years. This will be especially interesting for the earlier stage, smaller enterprises, and those investing in them who have always had to rely on a superstar, or guess (or maybe that's the same thing!). So...check it out! www.jtbd.one

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