Eric Pilkington
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About
Most enterprise AI dies between the pilot and the P&L. I rebuild the layer in…
Articles by Eric
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The Decision Dividend Has a Denominator
The Decision Dividend Has a Denominator
The Automation Curve, Issue No. 8 Every enterprise I work with can now tell me what a decision costs.
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The Borrowed Multiple: Why the Market Has Already Priced a Company You Have Not BuiltAug 24, 2026
The Borrowed Multiple: Why the Market Has Already Priced a Company You Have Not Built
Investors are increasingly paying for a company that does not yet exist. The Automation Curve · Issue No.
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The Innovation Paradox: Why AI Makes Ideas Cheap and Judgment ExpensiveAug 17, 2026
The Innovation Paradox: Why AI Makes Ideas Cheap and Judgment Expensive
The Automation Curve, Issue No. 5 Every enterprise I work with has largely solved AI adoption.
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The Productivity Trap: Why AI Transformation Keeps Missing the Top LineAug 12, 2026
The Productivity Trap: Why AI Transformation Keeps Missing the Top Line
Enterprises are shrinking the current business faster than they are building the next one. Two years into the…
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The Capacity Gap: Why AI Value Leaks Before It CompoundsAug 3, 2026
The Capacity Gap: Why AI Value Leaks Before It Compounds
The most persistent myth in enterprise AI is that automation lightens the load. The logic appears airtight: route the…
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The Judgment Deficit: What Enterprises Are Losing Faster Than They Are BuildingJul 27, 2026
The Judgment Deficit: What Enterprises Are Losing Faster Than They Are Building
Every CEO I speak with is running some version of the same experiment: deploy the copilots, stand up the agents…
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What the AI Performance Loop Gets BackwardJul 21, 2026
What the AI Performance Loop Gets Backward
Operational excellence and AI may be the products of competitive advantage—not a universally repeatable path to it. The…
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What the AI Performance Loop Gets BackwardJul 20, 2026
What the AI Performance Loop Gets Backward
Operational excellence and AI may be the products of competitive advantage—not a universally repeatable path to it. The…
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The Symbiotic Enterprise Has a Math ProblemJul 8, 2026
The Symbiotic Enterprise Has a Math Problem
Why the "AI-as-workforce" vision breaks down when you carry the numbers past the deck's front page. A wave of…
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The Consent Problem in the Age of AIMay 18, 2026
The Consent Problem in the Age of AI
Why the industry’s favorite story about its own future is colliding with the country that has to live in it Every…
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11K followers
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Eric Pilkington shared thisEvery enterprise I work with can now tell me what a decision costs. Almost none can tell me what it costs to stand behind one. That gap matters. The cost of generating decisions is collapsing. The cost of judgment, review, escalation, and accountability isn't. So the real constraint is shifting: it is no longer decision throughput. It is judgment coverage—the human capacity to govern the decisions systems now produce. That is the focus of the latest Automation Curve: The Decision Dividend Has a Denominator. It is the human capacity behind every decision. Cheap decisions are not the same as good economics. The dividend depends on who stands behind them. #TheAutomationCurve #AITransformation #Strategy #Leadership #FutureOfWork #AI UST Aravind Nandanan Manu Gopinath Sajesh Gopinath Ankur Sharma Mike Friedin Matt Hardy Haila Fine Mark Silva Elijah Kim Paul Genberg Robert Heckel Thomas Allgeyer Srinivas Gutta Lucas Warren Yana (. Jo Haley Jonathan Colehower Gregory Serrago Kristin Jacobson
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Eric Pilkington shared thisEuropean commerce is growing again. But the more important question is whether European retailers are growing in a market that is already changing beneath them. For years, the European retail playbook was shaped by a familiar set of constraints: fragmented markets, different languages and regulations, complex fulfillment networks, intense price competition, and consumers who were often slower to shift channels than their US or Asian counterparts. AI is now changing the economics of that model, and the payoff is becoming harder to ignore. Discovery is becoming less dependent on where a retailer ranks or how effectively it captures attention. Comparison is becoming easier. Product choice is becoming more automated. And the payoff is rising for clean product data, reliable availability, transparent pricing, and consistent fulfillment. That matters in Europe because complexity has always been part of the cost of doing business. AI can reduce some of that complexity, but only if retailers change how they make decisions across merchandising, pricing, marketing, supply chain, and customer experience, and capture the resulting gains. Otherwise, they risk putting faster technology on top of the same fragmented operating model. That is why I don't see Europe’s current e-commerce momentum primarily as a growth story. It is an operating-model story. The retailers that emerge stronger will be those that use AI not simply to automate existing processes, but to create a more connected commercial system that can sense demand, make decisions, and act across markets with far greater speed and convert that speed into better performance. I explore that shift, and the five forces reshaping European commerce, in a new piece for UST. The question for European retail leaders is becoming less about whether demand is returning and more about whether their organizations are built for the emerging market and ready to capture its upside. https://lnkd.in/gkbHhKai #EuropeanRetail #Ecommerce #Retail #IntelligentCommerce #ArtificialIntelligence #DigitalCommerce #AgenticAI #OperatingModel UST Gregory Serrago Jonathan Colehower Yana (. Lucas Warren Sajesh Gopinath Manu Gopinath Ankur Sharma Mike Friedin Matt HardyEurope’s AI Reset Is Not a Growth Story. It Is an Operating-Model Story.Europe’s AI Reset Is Not a Growth Story. It Is an Operating-Model Story.
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Eric Pilkington shared thisThe market may already be paying you for the AI company you say you are becoming, not the company you run today. That is very different from paying you for the company you run today. Across the market, valuations have moved ahead of current earnings. Investors are pricing in new revenue, different margin structures, AI-native offers, and operating models that many companies have described but have not yet built, creating a widening gap between price and present performance. I think of that gap as the borrowed multiple: the portion of enterprise value advanced against future performance and not yet justified by current results. The problem is that much enterprise AI investment still goes toward efficiency. Cost reduction matters. Restructuring matters. But neither, on its own, creates the new earnings model the market has already priced in. That distinction changes the question leaders should be asking. Not simply: What value is AI creating? But: Are we building the company the market has already priced? The latest Automation Curve looks at the $27 trillion value migration already underway, how to distinguish an earned multiple from a borrowed one, and the evidence markets increasingly expect before they collect that valuation premium. #ArtificialIntelligence #AI #EnterpriseAI #AIStrategy #Leadership #CorporateStrategy #Transformation #EnterpriseValue #TheAutomationCurve UST Aravind Nandanan Manu Gopinath Sajesh Gopinath Ankur Sharma Sidarth Nandanan Mike Friedin Matt Hardy Haila Fine Mark Silva Elijah Kim Paul Genberg Robert Heckel Thomas Allgeyer Srinivas Gutta Lucas Warren Yana (. Jo Haley Jonathan Colehower Gregory SerragoThe Borrowed Multiple: Why the Market Has Already Priced a Company You Have Not BuiltThe Borrowed Multiple: Why the Market Has Already Priced a Company You Have Not BuiltEric Pilkington
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Eric Pilkington shared thisAI has made it dramatically cheaper for organizations to generate ideas. It has not made it cheaper to know which ideas are worth pursuing. That distinction is becoming one of the most important strategic constraints on enterprise AI. A team that once had the time and resources to explore a dozen approaches can now generate hundreds or thousands. New products. New customer segments. New designs. New hypotheses. New ways to solve the same problem. The assumption is that more possibilities should produce more innovation. But generation is only one part of the system. Every additional possibility still has to be evaluated, tested, funded, absorbed, and ultimately commercialized. The people best equipped to make those calls usually have the deepest domain expertise, so judgment becomes the constraint that determines which ideas advance. So the bottleneck moves. As the cost of generating options approaches zero, the value of judgment rises. Organizations can suddenly produce far more possibilities than they can evaluate well, turning an innovation engine into a larger screening queue and making strategic selection the real bottleneck. I call this the innovation paradox. In the next issue of The Automation Curve, I look at what this means for enterprise AI strategy: why efficiency and discovery follow very different value curves, why most AI portfolios are still heavily weighted toward the former, and why the next advantage may come less from better models than from better systems for selecting, absorbing, and commercializing what those models produce. The question is no longer whether your organization can generate more with AI. It is whether it has built enough judgment capacity to know what deserves to win and to turn AI output into enterprise advantage, not just enterprise output. #ArtificialIntelligence #EnterpriseAI #Innovation #AIStrategy #GenerativeAI #Leadership #DigitalTransformation #TheAutomationCurve UST Aravind Nandanan Manu Gopinath Sajesh Gopinath Ankur Sharma Sidarth Nandanan Mike Friedin Matt Hardy Haila Fine Mark Silva Elijah Kim Paul Genberg Robert Heckel Thomas Allgeyer Srinivas Gutta Lucas Warren Yana (. Jo Haley Jonathan Colehower Gregory Serrago Alind GuptaThe Innovation Paradox: Why AI Makes Ideas Cheap and Judgment ExpensiveThe Innovation Paradox: Why AI Makes Ideas Cheap and Judgment ExpensiveEric Pilkington
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Eric Pilkington shared thisThe next issue of The Automation Curve examines a different reason enterprise AI is underdelivering. Most programs are still built around a familiar equation: automate work, remove cost, improve productivity. The scorecards reflect it. Hours saved. Cycle time reduced. Cost per case. Adoption. Utilization. Those measures matter. But they also shape the program. When AI is governed primarily as a productivity engine, the enterprise gets better at running the business it already has. What it does not necessarily get is new revenue, new categories, new customers, or a materially different business model. That is the productivity trap: enterprises are shrinking the current business faster than they are building the next one. In this issue, I look at why the gap between AI adoption and business impact is increasingly a target problem, not a technology problem. I explore the missing growth horizon in most transformation portfolios, the difference between using AI for substitution versus amplification, and why conventional AI scorecards continue to pull investment back toward cost. The central question is becoming less about whether AI is working and more about what we are asking it to work on. Are we using AI to make the current business cheaper, or to make the next business possible? 👇 #TheAutomationCurve #EnterpriseAI #ArtificialIntelligence #AITransformation #BusinessStrategy #Leadership #OperatingModel #GrowthStrategy #DigitalTransformation #FutureOfWork UST Aravind Nandanan Manu Gopinath Sajesh Gopinath Ankur Sharma Sidarth Nandanan Mike Friedin Matt Hardy Haila Fine Mark Silva Elijah Kim Paul Genberg Robert Heckel Thomas Allgeyer Srinivas Gutta Lucas Warren Yana (. Jo Haley Jonathan Colehower Gregory SerragoThe Productivity Trap: Why AI Transformation Keeps Missing the Top LineThe Productivity Trap: Why AI Transformation Keeps Missing the Top LineEric Pilkington
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Eric Pilkington shared thisThe next issue of The Automation Curve examines a constraint that sits behind many disappointing enterprise AI results. The prevailing assumption is that automation creates capacity. Remove routine work, return time to employees, and convert the savings into productivity. But that is often not what happens. AI removes the predictable middle of a job and leaves the exceptions, judgment calls, and consequential decisions behind. Work volume falls, but the cognitive density of the remaining work rises. The business case records hours released, while the people within the workflow experience greater pressure, slower decision-making, and less room to challenge the machine. I call this the capacity gap: the widening distance between the volume and complexity of decisions AI creates and the human capacity available to absorb them well. This is not primarily a wellness issue. It is an operating constraint, comparable to compute, data quality, or integration debt. Left unaddressed, it explains why successful pilots stall at scale and why modeled AI value often leaks before it reaches the P&L. In this issue, I explore how the capacity gap forms, why conventional AI scorecards miss it, and how leaders can redesign work, recovery, focus, and measurement around a simple principle: Human capacity must be provisioned with the same discipline as machine capacity. 👇 #TheAutomationCurve #EnterpriseAI #ArtificialIntelligence #AITransformation #FutureOfWork #OperatingModel #Leadership #HumanCapital #BusinessStrategy #DigitalTransformation #TheAutomationCurve #EnterpriseAI #ArtificialIntelligence #AITransformation #FutureOfWork #OperatingModel #Leadership #HumanCapital #BusinessStrategy #DigitalTransformation UST Aravind Nandanan Manu Gopinath Sajesh Gopinath Ankur Sharma Sidarth Nandanan Mike Friedin Matt Hardy Haila Fine Mark Silva Elijah Kim Paul Genberg Robert Heckel Thomas Allgeyer Srinivas Gutta Lucas Warren Yana (.The Capacity Gap: Why AI Value Leaks Before It CompoundsThe Capacity Gap: Why AI Value Leaks Before It CompoundsEric Pilkington
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Eric Pilkington reposted thisEric Pilkington reposted this$74B in annualized AI revenue. These same buyers kill 40% of their agents by 2027. Enterprises are scaling agents faster than they can govern them. Artificial Intelligence is now AI & Agentic Systems. Agentic AI is now the fastest-growing part of the AI conversation. We're still tracking AI adoption, governance, infrastructure, enterprise use cases, and business impact, now with agentic systems named as their own thread. Hope you'll like it! 72 posts on AI & Agentic Systems this week. 36 new voices in the mix. Here's what stood out to us at Frenus GmbH, where we track emerging market signals across AI adoption, governance, infrastructure, enterprise use cases, and business impact: Token prices fell 10x last year, yet enterprise AI spend tripled anyway Local-first inference is already cutting those costs by 70% for the companies moving first Seat-based pricing is projected obsolete by 2028 as value shifts from tool access to decision accuracy An agent 95% reliable per step finishes only 54% of a 12-step workflow, per MIT's pilot data Agent governance is emerging as its own discipline: identity, permissions, scope, runtime enforcement, revocation 61% of senior leaders abandoned an AI project this year over workforce skills gaps, not tool gaps No one assigns an owner to an agent until after it's already made a mistake. Featured read: Oliver Patel, AIGP, CIPP/E, MSc maps four leading frameworks for governing agentic AI: Singapore's IMDA foundations, the World Economic Forum's adoption guardrails, OWASP's security risk mitigations, and Singapore's SAFR runtime safeguards. Together they give enterprises a starting blueprint instead of a blank page. Budgeted for agent governance, or still hoping IT covers it? Thanks for being part of this edition. Enjoy the read!Best of LinkedIn CW 29/ 30: AI & Agentic SystemsBest of LinkedIn CW 29/ 30: AI & Agentic SystemsThomas Allgeyer
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Eric Pilkington shared thisMost enterprises are measuring what AI adds: faster cycles, lower costs, greater throughput, and more decisions made with machines in the loop. Far fewer are measuring what AI may be quietly taking away. This second issue of The Automation Curve examines what I call the judgment deficit: the widening gap between the number of decisions an enterprise makes with AI and the number of people inside the organization who could still make those decisions well without it. The risk is not that AI replaces routine work. Much of that work should be automated. The risk is that enterprises also remove the experiences through which people learn to recognize patterns, challenge assumptions, and know when the machine is wrong. Productivity is a flow. Judgment is a stock. An organization can improve the first while steadily depleting the second. The companies that win the next decade will not simply have the most agents. They will have people who can tell when those agents are wrong, and an operating model that gives them the authority to act. In the second issue of The Automation Curve: How AI changes competition, organizations, and enterprise strategy, I explore why enterprises need to build a second infrastructure for AI, one designed not only to capture productivity, but to preserve judgment. The Judgment Deficit: What Enterprises Are Losing Faster Than They Are Building #TheAutomationCurve #AI #ArtificialIntelligence #EnterpriseAI #AIStrategy #FutureOfWork #Leadership #OperatingModel #DigitalTransformation UST Aravind Nandanan Sajesh Gopinath Manu Gopinath Ankur Sharma Sidarth Mike Friedin Matt Hardy Haila Fine Mark Silva Elijah Kim Paul GenbergThe Judgment Deficit: What Enterprises Are Losing Faster Than They Are BuildingThe Judgment Deficit: What Enterprises Are Losing Faster Than They Are BuildingEric Pilkington
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Eric Pilkington shared thisWelcome to The Automation Curve. Every major technology shift changes more than technology. It changes how companies compete, organize, and create value. This newsletter explores those changes through the lens of enterprise strategy, economics, and AI transformation. Thanks for reading—and welcome aboard.What the AI Performance Loop Gets BackwardWhat the AI Performance Loop Gets BackwardEric Pilkington
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Eric Pilkington liked thisEric Pilkington liked thist's never been a more exciting time to be a tech executive, and robotics is the thing that has my attention these days. Not because of the demo videos. Because of a number I can't stop thinking about: 9 minutes. That's how long Agility Robotics' new Digit 5 takes to charge for 90 minutes of work. It will never make a headline. But anyone who has run a warehouse floor knows it's the difference between a demo and a shift. The bigger story is who Digit 5 is designed for. It works "cage-free" beside people—an independent safety controller, human detection, light and sound cues so the person next to it always knows what it's about to do. Agility has $300M+ in orders from GXO, Schaeffler, Amazon and Toyota, and is lining up a public listing. A startup betting everything on one idea: the robot's job is to fit into a human workplace, not the other way around. In my experience, digital projects fail for one main reason—they were built around the technology and the people were expected to adapt. The ones that worked started with the person doing the job. Robotics is about to learn the same lesson, and I think the winners will be companies you haven't heard of yet. Will it be a startup, or one of the big players that win the robotics race? #Robotics #Humanoids #FutureOfWork #DigitalTransformation
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Eric Pilkington liked thisEric Pilkington liked thisHey, Cincinnati! 👋 We're coming to you live at BLINK Festival this year and we're bringing a special friend with us! Introducing Vurvey’s AI Brand Companion Blinky, the official AI companion of the BLINK festival. Blinky can help you get closer to the artists and discover the unique perspectives, details and experiences that make BLINK artists human. What is BLINK? A free, four-night festival of light and art that draws millions every year. Join us October 8-11 across Cincinnati with projection mapping, murals, and light installations spread over 30 city blocks. We make AI that celebrates the magic of humanity, so a festival built around artists feels like the right place to show what that looks like. Learn more here: https://lnkd.in/eFJ_XFQT #AIPoweredByPeople #BLINK2026 #Blinky #VurveyLabs #Cincinnati
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Eric Pilkington liked thisEric Pilkington liked thisMostly it’s about product and desire. Invest in product! Neem London
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Eric Pilkington liked thisEric Pilkington liked thisThis has been one of the most fun papers I have written. Not because governance is usually a crowd pleaser. 😄 It is typically viewed as necessary overhead: more controls, more review, more friction. Yet runtime governance at the reasoning layer can do more than manage risk. Before an agent’s interpretation becomes action, it can prevent some of the reasoning, tool use, downstream work, review and remediation that would otherwise follow an interpretation the institution did not authorize. Token prices are falling. Systems can reason more, call more tools, hand work to other agents and keep going longer. What matters is not only the cost of an inference, but what an interpretation is allowed to set in motion. Preventable Computation describes the additional work that could have been avoided if an available runtime control had held an unauthorized interpretation before it entered execution. And then there is Governed Semantic Reuse. Instead of making an AI system reconstruct the same approved institutional meaning in broad prompt context every time it encounters a familiar kind of decision, previously authorized meaning can be reused under matching runtime conditions. The facts may change, but an organization should not have to keep teaching the system what its own approved terms, policies and rules mean. Less repeated context. Less unnecessary inference. And perhaps a small but meaningful contribution to “green computation.” My Executive Brief is below. #ai #token #tokenomics #cost #aiusage #ArtificialIntelligence #AIGovernance #ResponsibleAI #AgenticAI #EnterpriseAI #CyberSecurity #RiskManagement #GRC #AISafety #AIsecurity #AIArchitecture #preventablecomputation #GovernedSemanticReuse #greencomputationPreventative Computation: The Economics of Agentic AIPreventative Computation: The Economics of Agentic AIMaureen Doyle-Spare
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Eric Pilkington liked thisEric Pilkington liked thisJanuary isn’t a strategy - it's a date on the calendar... I've been in this cylce a long time and one of the stranger things about advertising: some companies spend all year building awareness… then pull back in Q4 and decide they’ll “really get after it” in January. Not sure about you, but I don't know anyone that resets on January 1st (unless you count the fake resolution that lasts a week). If you want 2027 to start strong, start building that momentum now. Stay visible. Stay relevant. Take advantage of the noise getting quieter around you. And because repitition matters... January isn’t a strategy - it's a date on the calendar.
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Eric Pilkington reacted on thisEric Pilkington reacted on thisI can proudly say that my mom, Mary Ellen Guest, made a huge impact on me, my brother, her grandkids, my family, her friends, and countless others across the City of Chicago and the State of Illinois. She gave herself selflessly to all of us, always with a huge smile on her face, ready to give a huge hug, quick to laugh, and always asking what she could do and how she could help. She was equally amazing in the kitchen baking desserts as she was in the political and non-profit strategy arenas. This past weekend, she died after battling more serious illness for a few weeks in addition to a yearslong decline brought on by multiple chronic conditions. We will always remember her and carry on her legacy of love, warmth, and the commitment to progress and justice. Her obituary can be found below and I look forward to seeing those who can make it to celebrate her life and grieve together. https://lnkd.in/gur6PASd
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Eric Pilkington liked thisEric Pilkington liked thisWhat drives desire and where does AI collide with human understanding on this topic? Most importantly, what are brands getting wrong here? Chad Reynolds, our CEO, sat down to discuss this and much more with Mark Sinnock, Global Chief Strategy, Data and Innovation Officer at Havas Creative, Sarah Collinson, CEO of Havas New York, and Jackie Lyons, Chief Planning Officer Havas Media for the Decoding Desire in the Age of AI Summit in NYC. Thanks to Havas and Brand Innovators for putting this group together.
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Eric Pilkington liked thisEric Pilkington liked thisHeading to Dirty Jobs in San Francisco tomorrow (September 23rd) obo LVLON and ALAi Tech Inc. At ALAi we use AI to discover new molecules in materials science, so I want to meet anyone whose hardware is quietly waiting on a better material. Say hello if you're going, and let's 'Talk Dirty!' thank you Vijay Chattha and Jay Kapoor for getting a bunch of living legends in the same room and letting me in to be a counter weight and balance out the high IQ ;-)
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Eric Pilkington liked thisEric Pilkington liked thisAdvertising is having yet another identity crisis. Agencies are reinventing themselves again and again. New operating models. New acronyms. AI transformation. Creator ecosystems. Cultural intelligence. Experience orchestration. Another new CEO uses language that makes the latest reorganization, tech evolution, and layoffs sound like the invention of electricity. Executives take the stage to explain the future of marketing with the solemnity of people negotiating nuclear treaties. Holding companies spend fortunes restructuring themselves to become more agile, integrated, intelligent and modern. And then somebody makes that ad. Novig. Callaway. Pick your example. The names almost don’t matter because another one will be along shortly. The formula has become depressingly familiar: take gambling, sex, humiliation, misogyny, stupidity or some combination thereof; wrap it in irony; call it culturally provocative; wait for people to complain; celebrate the engagement; and, if necessary, have the CEO issue the ceremonial apology explaining that the work “missed the mark.” Maybe it didn’t miss the mark. Maybe this is the mark now. That is the more uncomfortable possibility. Because some of this advertising works. People notice it, share it, and argue about it. Search goes up. Earned media follows. The algorithm does exactly what the algorithm was designed to do. But effectiveness and value are not the same thing. A fistfight in a restaurant attracts attention too. That doesn’t make it hospitality strategy. The industry spent decades arguing that brands have meaning, purpose and cultural influence. Fine. Then it cannot suddenly claim moral neutrality when degrading behavior happens to generate impressions. Corporations are underwriting culture whether they acknowledge it or not. Every media dollar rewards something. Every sponsorship legitimizes something. Every campaign makes a small argument about what deserves our attention. Increasingly, advertising seems willing to make that argument from the gutter. The irony is almost perfect. We have never possessed more sophisticated technology for understanding human behavior, predicting preference, personalizing communication, and measuring response. And somehow all that intelligence keeps producing more sophisticated ways to make people look. That isn’t creative bravery. It’s attention arbitrage. Perhaps the next reinvention of advertising doesn’t require another operating model, AI platform or Cannes panel about the future. Perhaps it begins with remembering that getting someone’s attention was never supposed to be the highest ambition of the business. Because when an industry becomes willing to do almost anything to be noticed, the question eventually stops being whether the advertising worked. The question becomes what the industry is becoming in order to make it work.
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“The sage advice passed down to me upon graduation was to not focus so much on your first job, but rather whom you choose to be your first boss. To say I hit the jackpot with Eric Pilkington is an understatement. In a little more than a year, I came to know Eric both as a manager and mentor - his unmatched work ethic, infectious passion and curiosity for all things mHealth, and incredibly varied experience in advertising made him the ideal role model. Detail-driven and exceptionally insightful in his approach, Eric worked tirelessly to push the boundaries of what’s possible in digital health – always at the delight and awe of both clients and internal folks. I count myself truly lucky to have learned from and worked with Eric; he is undoubtedly one of a kind.”
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