Emanuel Rose

https://youtu.be/YHIWCHbn-x8 AI creates its highest strategic value when leaders move beyond content production and apply it to operations, fulfillment, and scalable offers. The question is not “How do we use AI?” but “Which repeatable process can we responsibly turn into a useful system?” Start with low-risk, repeatable tasks before automating high-stakes workflows. Document your intellectual property before trying to turn it into an AI product. Use simple prototyping tools to test demand, workflow logic, and stakeholder buy-in. Build human review, privacy, and security thinking into the offer from the beginning. Position AI as an augmentation layer, not a replacement for your expertise. Use AI-powered fulfillment to create accessible lower-ticket offers without diluting premium services. The IP-to-AI Fulfillment Loop Step 1: Identify the process you already repeat with clients, donors, members, or stakeholders. Look for the work that follows a pattern: intake questions, diagnosis, prioritization, story extraction, follow-up, outline creation, or guided planning. Step 2: Interview yourself or have someone interview you about how you move a person from point A to point B. Capture the exact questions you ask, the signals you listen for, the decision points you use, and the outputs you create. Step 3: Turn that thinking into a written workflow before building anything. A clear process document becomes the bridge between human expertise and an AI-enabled experience. Step 4: Create a lightweight prototype using tools such as Lovable, Replit, or similar builders only to test the concept. The goal is not perfection; the goal is to see whether the workflow makes sense, produces useful output, and earns real feedback. Step 5: Add guardrails before scaling the system. Decide what data is collected, where it is stored, when the AI should stop, when a human should step in, and what topics are outside the tool’s purpose Step 6: Package the AI system as an extension of your expertise, not a substitute for it. This can create a lower-ticket pathway, membership asset, onboarding tool, or fulfillment layer that increases capacity while keeping the premium human offer intact. From Content Creation to AI-Powered Fulfillment AI Layer Strategic Question Best Use Case Leadership Risk Content Creation How can we produce useful communication with less friction? Drafting posts, emails, outlines, images, summaries, and campaign assets. Generic messaging that weakens voice, trust, and differentiation. AI Operations Which internal workflows can be made easier, clearer, or more consistent? Follow-ups, email review, invoice support, CRM updates, task review, and planning prompts. Automating unclear processes before the team understands ownership and review points. AI-Powered Offers How can our intellectual property become a scalable client or member experience? Coaching tools, guided assessments, knowledge apps, framework builders, and membership enhancements. Scaling too soon without requirements, security, user testing, or human oversight. Leadership Questions for Responsible AI Offers What part of our expertise is structured enough to become a guided experience?  Leaders should look for proven methods, recurring client conversations, and repeatable frameworks that already produce outcomes before introducing AI into fulfillment. Where does human judgment remain essential?  AI may help ask questions, organize responses, and generate useful drafts, but sensitive situations, strategic nuance, crisis signals, and final interpretation should remain under human supervision. Are we building for validation or scale?  A prototype should help leaders learn quickly, gather feedback, and refine requirements; a scalable product requires stronger architecture, security, documentation, and support. Can users understand what the tool does and does not do?  Clear expectations build trust. The system should explain its role, its limits, and when a user should connect with a person instead of continuing through automation. Does the AI offer strengthen the business model?  The best applications expand access to expertise, create a stepping-stone into higher-value services, and keep the organization’s mission and voice visible throughout the experience. Author: Emanuel Rose, Senior Marketing Executive, Strategic eMarketing Contact: https://www.linkedin.com/in/b2b-leadgeneration/ Last updated: Marketing in the Age of AI transcript featuring Kimberly Inez Mays. Kimberly Inez Mays LinkedIn profile: https://www.linkedin.com/in/kimberlyinezmays/ Guest notes provided for Kimberly Inez Mays. Strategic eMarketing company information: https://strategicemarketing.com/about About Strategic eMarketing: Strategic eMarketing helps B2B and mission-driven organizations clarify positioning, build trust-based marketing systems, and apply AI in practical ways that support measurable growth. https://strategicemarketing.com/about https://www.linkedin.com/company/strategic-emarketing https://podcasts.apple.com/us/podcast/marketing-in-the-age-of-ai-with-emanuel-rose/id1741982484 https://open.spotify.com/show/2PC6zFnFpRVismFotbNoOo https://www.youtube.com/channel/UCaLAGQ5Y_OsaouGucY_dK3w Guest Spotlight Guest: Kimberly Inez Mays LinkedIn: https://www.linkedin.com/in/kimberlyinezmays/ Company: Not specified in source materials Podcast episode link: Not provided in source materials Kimberly Inez Mays is an AI strategist, speaker, and author who helps nonprofit leaders, coaches, and thought leaders integrate artificial intelligence without sacrificing mission, voice, or humanity. Her work connects CRM systems, marketing automation, operational workflows, and AI governance frameworks for organizations focused on social good and authentic leadership. About the Host Emanuel Rose is a senior marketing executive and the host of Marketing in the Age of AI, where he helps leaders turn AI from a confusing add-on into a practical advantage through clearer messaging, stronger trust, and smarter systems. Connect with Emanuel on LinkedIn: https://www.linkedin.com/in/b2b-leadgeneration/ Build the Small System Before the Big Platform The practical move is to choose one repeatable piece of expertise, document it, and test it as a guided AI workflow with a small group of users. Once the process proves useful, leaders can add governance, security, and packaging so the system becomes a durable business asset rather than another tool experiment. Watch the podcast episode featuring Kimberly Mays: https://youtu.be/YHIWCHbn-x8

https://youtu.be/lCy9iK2vDTw AI has lowered the cost of building software, so the strategic advantage now goes to the person who understands the customer, the workflow, and the problem worth solving. The smartest leaders aren’t chasing giant funding rounds first; they use AI to validate thin products, protect risky workflows, and turn proof into leverage. Build the thinnest working version before investing in perfection. Use AI tools to test demand, not to avoid customer conversations. Keep a human review layer on anything tied to customer data, payments, trust, or compliance. Treat massive infrastructure investment as a subsidy for builders operating closer to the customer. Measure progress by revenue per employee, not headcount or software complexity. Own the workflow and customer insight because the building barrier is falling. Raise capital against proof, not against theory. The Thin Build Revenue Loop Step 1: Start with the paid pain, not the tool. The best AI product ideas come from a specific problem customers already spend time, money, or political capital trying to remove. Step 2: Translate that pain into one clear task. If the product cannot be described as one job it performs well, it is too early to scale, pitch, or layer in complexity. Step 3: Build the thinnest possible version with a no-code or AI-assisted tool. The first version is not a monument; it is a question asked of real users. Step 4: Put it in front of ten real people. Internal users count if they have the problem, feel the cost, and would benefit from the workflow being improved. Step 5: Separate shrug behavior from dependency behavior. If people shrug, you saved months of waste. If they cannot live without it, you have identified the small part worth rebuilding with discipline. Step 6: Add human review where the product can cause harm. AI can draft, route, research, and assemble, but people must sign off where money, customer data, reputation, or safety are involved. Where the AI Advantage Actually Sits Strategic Path Capital Requirement Real Advantage Primary Risk Control AI infrastructure and compute Billions in data centers, chips, and capacity Control of the plumbing beneath AI products Scale, contracts, and execution discipline No-code workflow products Low initial build cost with browser-based tools Customer knowledge, process ownership, and speed to proof Human review on sensitive workflows AI agent security Focused funding tied to a specific enterprise problem Trust layer for agent adoption inside companies Screening, permissions, add-on review, and governance Strategic Questions Leaders Should Ask Before Building Where does the moat move when building gets cheap? The moat moves away from technical access and toward judgment. The leaders with an edge are the ones who know the customer, the workflow, the buying trigger, and the moment when the product becomes indispensable. When should a leader ignore technical debt? In the first version, technical debt is often less dangerous than building the wrong product perfectly. If the product is still testing whether anyone cares, speed matters; once customers prove dependency, the critical 5% can be rebuilt properly. What should never be fully delegated to AI? Anything that can hurt a customer, expose private data, move money, damage reputation, or create compliance risk needs human oversight. The right operating model is AI for leverage and people for accountability. Why are giant AI funding rounds not the model for most teams? Most teams are not building compute infrastructure. The better model is proving a narrow customer problem, generating revenue, and using capital only when it accelerates something the market has already validated. What number should leaders watch more closely than headcount? Revenue per employee is becoming a sharper signal of operating quality. AI gives lean teams leverage, which means bloated process and unnecessary staffing are harder to defend. Author: Emanuel Rose, Senior Marketing Executive, Strategic eMarketing Contact: https://www.linkedin.com/in/b2b-leadgeneration/ Last updated: Crunchbase funding concentration data cited in the episode transcript. Veracode AI coding security test results cited in the episode transcript. Y Combinator AI-generated software adoption statistic cited in the episode transcript. Stripe Atlas solo founder formation statistic cited in the episode transcript. Company examples discussed: Lovable, Replit, Clay, AIR, TurboPuffer, Stan, and Flocker. About Strategic eMarketing: Strategic eMarketing helps founders, executives, and growth teams turn AI, authentic messaging, and practical marketing systems into measurable business development outcomes. https://strategicemarketing.com/about https://www.linkedin.com/company/strategic-emarketing https://podcasts.apple.com/us/podcast/marketing-in-the-age-of-ai-with-emanuel-rose/id1741982484 https://open.spotify.com/show/2PC6zFnFpRVismFotbNoOo https://www.youtube.com/channel/UCaLAGQ5Y_OsaouGucY_dK3w About the Host Emanuel Rose is a senior marketing strategist, author, and host of Marketing in the Age of AI, where he helps leaders apply AI without losing the human trust that drives growth. Connect with him on LinkedIn at https://www.linkedin.com/in/b2b-leadgeneration/. Build the Smallest Proof That Can Change the Conversation Pick one customer problem that has a clear economic cost and build a thin version of the solution this week. Do not wait for a perfect plan, a large budget, or a full engineering cycle; get the workflow into a user’s hands and let behavior tell you what deserves more investment. Watch the podcast episode: https://youtu.be/lCy9iK2vDTw

https://youtu.be/JM1ykp4ap4Y AI is changing experiential marketing by turning event attention into personalized content, compliant data capture, and measurable follow-up. The strongest leaders will stop treating activations as booth entertainment and start designing them as conversion systems. Use AI only when it strengthens the brand promise, audience participation, or measurable business outcome. Build activations around shareable identity: people post content when they see themselves inside the story. Replace passive booth traffic with QR-based participation that captures interest without creating friction. Extend event ROI by keeping digital activations live after the venue closes. Balance physical artifacts, such as custom trading cards, with digital assets that scale to larger audiences. Measure behavior, not applause: scans, uploads, shares, hashtag use, opt-ins, and follow-up performance matter. Treat robotics as a high-attention entry point, then connect that attention to a clear marketing system. The Live-to-Lead Activation Loop Step 1: Start with the business outcome before selecting the technology. If the goal is lead generation, the activation must include compliant data capture, a reason to opt in, and a follow-up path that sales or marketing can use. Step 2: Design the moment around personal relevance. AI video, custom trading cards, robotics, and photo activations work best when the participant becomes the subject, not a spectator watching a brand perform. Step 3: Reduce friction at the point of participation. QR workflows can expand an activation beyond the booth, letting people participate from a line, a seat, a conference floor, or a social channel after the event. Step 4: Create a share trigger that feels native to the audience. A branded image is not enough; the content must give people a reason to post, such as humor, aspiration, novelty, status, fandom, personalization, or a contest incentive. Step 5: Capture clean data with clear consent. Experiential marketing becomes far more valuable when the brand can connect participation to permission-based follow-up rather than treating the event as a one-time impression. Step 6: Extend the campaign window beyond the physical event. A three-day trade show can become a thirty-day content and lead-generation campaign when the activation is digital, reusable, and easy to distribute across LinkedIn, Instagram, email, and partner channels. From Booth Novelty to Marketing Asset Activation Choice Best Strategic Use Primary Risk Leadership Takeaway Humanoid robots Creating high-attention moments, social recording, booth traffic, stage interaction, and brand memorability Using novelty without a path to capture data or move prospects into a next step Use robotics as the opening act, not the whole campaign. AI cinematic content Putting attendees into personalized videos, branded scenes, fan experiences, or campaign narratives Producing content people see once but do not share or connect back to the brand Make the attendee the hero while keeping the brand visibly tied to the experience. QR-based digital activations Scaling participation, avoiding lineups, reducing staffing needs, extending campaigns, and collecting compliant leads Making the experience feel generic if the creative is not tied to audience identity Use QR access to turn event space limits into audience-scale participation. Five Leadership Questions for Smarter Event ROI How do we know whether an activation deserves investment before the event begins?  Define the measurable job of the activation: qualified leads, social reach, product education, booth dwell time, customer appreciation, partner visibility, or content creation. If the team cannot name the job, the technology will become decoration. What makes people share branded event content voluntarily? People share content that says something about them. The strongest event assets give attendees a story, identity, status signal, or playful transformation they want others to see.  Where does AI create real leverage in live marketing?  AI creates leverage when it turns one creative concept into many personalized outputs at scale. A single campaign theme can become thousands of individualized images, videos, or trading-card-style assets with brand consistency. :Why do physical takeaways still matter when digital sharing is easier?  Tangible personalized items have staying power. A custom card, engraved notebook, or printed keepsake can remain on a desk, shelf, or in a conversation long after the event ends. What should executives ask vendors before approving an AI activation?  Ask how the experience captures consent, what data is collected, how long the activation can remain live, what reporting is available, how brand assets are protected, and what happens after the first scan, upload, or share. Author: Emanuel Rose, Senior Marketing Executive, Strategic eMarketing Contact: https://www.linkedin.com/in/b2b-leadgeneration/ Last updated: Richard Foltys LinkedIn profile: https://www.linkedin.com/in/416rich DMA Events guest notes and interview transcript Buy and Rent Robots: buyandrentrobots.com Marketing in the Age of AI podcast: Apple Podcasts and Spotify About Strategic eMarketing: Strategic eMarketing helps B2B organizations clarify their message, build trust, and turn AI-enabled marketing systems into practical growth assets. https://strategicemarketing.com/about https://www.linkedin.com/company/strategic-emarketing https://podcasts.apple.com/us/podcast/marketing-in-the-age-of-ai-with-emanuel-rose/id1741982484 https://open.spotify.com/show/2PC6zFnFpRVismFotbNoOo https://www.youtube.com/channel/UCaLAGQ5Y_OsaouGucY_dK3w Guest Spotlight Guest: Richard Foltys LinkedIn: https://www.linkedin.com/in/416rich Company: DMA Events Podcast episode link: Not provided Richard Foltys is the founder of DMA Events, an international experiential events company specializing in interactive brand activations, photo and video experiences, AI technology, custom trading cards, robotics, and live event entertainment. With more than 25 years of experience and over 1,500 events, he has worked with global brands, agencies, government organizations, and members of the Royal Family. About the Host Emanuel Rose is a senior marketing executive and the voice behind Marketing in the Age of AI, where he helps leaders turn AI from a confusing add-on into a practical advantage for messaging, trust, and growth systems. Connect with Emanuel on LinkedIn: https://www.linkedin.com/in/b2b-leadgeneration/ Make Every Live Moment Accountable The next event budget should not be judged by booth size or visual spectacle alone. Choose one clear outcome, design a personalized participation path, capture permission-based data, and keep the activation running after the floor closes. AI, robotics, and interactive media are valuable when they help the right audience take the next step. Start with strategy, then select the technology that makes participation easier, content more shareable, and follow-up more measurable. Watch the podcast episode featuring Rich E Foltys: https://youtu.be/JM1ykp4ap4Y

https://youtu.be/jmtE3mtG7k0 AI becomes a practical advantage only when leaders connect it to business outcomes, secure data access, and teamwide adoption. The strongest companies will move from individual experimentation to governed systems that scale trust, efficiency, and growth. Start with the business result before selecting AI tools or agents. Treat cybersecurity as an enabler of AI adoption, not a blocker. Build data foundations before giving teams broad access to automation. Move from individual tool preference to organization-wide operating discipline. Define success metrics beyond generic efficiency gains. Review SaaS overlap before adding more AI-enabled software. Use small, repeatable workflow wins to create internal confidence. The Secure AI Growth Loop Step 1: Clarify the business outcome. Before budget, tools, or vendors enter the conversation, leadership needs to define what good looks like: fewer hires required during growth, better response times, stronger customer support, controlled risk, or improved operating leverage. Step 2: Map where work repeats across the team. The best AI opportunities are often hiding in shared operational friction: reporting, customer service, sales follow-up, document handling, or knowledge retrieval. Leaders should look across roles, not just at one employee’s preferred tool. Step 3: Secure the data foundation. AI agents and custom apps are only as safe as the data access behind them. Governance, permissions, and cybersecurity practices determine whether AI becomes a scalable advantage or an uncontrolled exposure point. Step 4: Choose use cases with measurable value. Efficiency can matter, but it is not always the real success metric. For some organizations, the better measure is whether they can grow revenue without adding the same number of people, tools, or process layers. Step 5: Reduce tool sprawl before adding automation. Many companies already carry overlapping SaaS subscriptions across sales, marketing, operations, and service. AI strategy should include simplification, because adding agents on top of messy systems only compounds the mess. Step 6: Scale from the individual to the organization. AI adoption cannot stay trapped in “my favorite tool.” The leadership move is to ask how one useful workflow can be governed, documented, secured, and expanded for the benefit of the whole team. From Tool Chasing to Governed AI Adoption Leadership Approach Common Behavior Business Risk Strategic Shift Tool-first AI Teams add agents, apps, and AI features without a clear plan. Data exposure, weak adoption, duplicated systems, and unclear ROI. Define outcomes, ownership, permissions, and measurement before deployment. Individual-first AI One person finds a model or workflow they like and keeps it isolated. The organization gains pockets of productivity but no durable operating advantage. Convert useful individual workflows into secure team systems. Security-last AI Cybersecurity enters the conversation only after tools are already in use. Company data may move into unsupervised apps or custom builds. Use cybersecurity as the structure that lets more people use AI safely. Leadership Questions That Separate AI Value from AI Noise What outcome would make this AI initiative worth the organizational effort?  Leaders should name the measurable result before discussing platforms. A clear target prevents AI from becoming another disconnected technology project. Which workflows are repeated across teams and create the most drag?  These are often better starting points than one-off experiments. Shared friction creates shared value when solved well. Where does company data go when employees build or use AI tools?  This question needs a direct answer. If leaders cannot see the data path, they cannot manage trust, compliance, or customer risk. Are we measuring efficiency, scalability, revenue capacity, or cultural protection?  Each metric leads to a different implementation plan. AI success should match the leader’s actual growth problem. Which current software tools can be consolidated before we add more? AI-enabled SaaS costs will continue to pressure budgets. Tool rationalization is now part of AI strategy. Author: Emanuel Rose, Senior Marketing Executive, Strategic eMarketing Contact: https://www.linkedin.com/in/b2b-leadgeneration/ Last updated: Marketing in the Age of AI transcript featuring Brian Beck. Guest briefing notes provided for Brian Beck and Proxurve Solutions. Brian Beck LinkedIn profile: https://www.linkedin.com/in/brianbeck73/ Proxurve Solutions contact reference from guest materials: bbeck@proxurve.com About Strategic eMarketing: Strategic eMarketing helps business leaders, B2B teams, and growth-focused organizations build authentic marketing systems that connect strategy, content, AI adoption, and measurable revenue outcomes. https://strategicemarketing.com/about https://www.linkedin.com/company/strategic-emarketing https://podcasts.apple.com/us/podcast/marketing-in-the-age-of-ai-with-emanuel-rose/id1741982484 https://open.spotify.com/show/2PC6zFnFpRVismFotbNoOo https://www.youtube.com/channel/UCaLAGQ5Y_OsaouGucY_dK3w Guest Spotlight Guest: Brian Beck LinkedIn: https://www.linkedin.com/in/brianbeck73/ Company: Proxurve Solutions Podcast episode link: Not provided in the source materials. Brian Beck works with executives to make AI, cybersecurity, and business technology easier to understand and more useful inside the organization. His focus is practical: turning complex systems into growth, efficiency, and stronger security while helping leaders navigate change with clarity. About the Host Emanuel Rose is a senior marketing strategist, author, and host of Marketing in the Age of AI, where he helps leaders turn AI into clearer messaging, stronger trust, and smarter business systems. Connect with Emanuel on LinkedIn: https://www.linkedin.com/in/b2b-leadgeneration/ Apply the Secure Adoption Mindset This Week Pick one recurring workflow, define the business result, and identify what data the process touches before choosing an AI tool. Then bring marketing, operations, IT, and leadership into the same conversation so adoption serves the whole organization, not just the person who found the newest feature. Watch the podcast episode featuring Brian Beck: https://youtu.be/jmtE3mtG7k0

https://youtu.be/pr7VWEfdogM Direct mail is no longer a blind offline channel when it is paired with AI, address resolution, campaign-specific data, and disciplined measurement. The strategic play is to treat physical mail as a measurable performance channel that earns its place through response lift, engagement quality, and repeatable testing. Use direct mail where the economics justify it, especially high-consideration offers with average order values above $1,000. Measure engagement separately from closed revenue so marketing and sales accountability remain clear. Plan mail as a repeated sequence, not a one-time drop, with multiple impressions layered around the household. Attach attribution to the individual campaign through QR codes, phone numbers, landing pages, device targeting, and address resolution. Use AI to review creative, targeting, timing, and competitor activity before money goes into print and postage. Compare results against similar campaigns, verticals, and ZIP codes rather than relying on generic benchmarks alone. Build tests with a clean methodology so memory effects, repeated households, and weak controls don’t distort the data. The Measurable Mail Growth Loop Step 1: Start with the economics. Direct mail has real production and postage costs, so it belongs where a small response lift can create meaningful revenue impact. High-ticket categories such as education, finance, home services, nonprofits, healthcare, luxury retail, and professional services tend to have more room for profitable acquisition. Step 2: Define the household and offer match before creative gets built. The best mail piece cannot overcome poor targeting or a weak reason to act. Leaders should ask whether the recipient, the offer, the timing, and the expected customer value all line up before approving a campaign. Step 3: Build attribution into the campaign from the beginning. Unique QR codes, dedicated phone numbers, personalized landing pages, text response, informed delivery links, and address resolution give the team real-time signals that old-school mail campaigns never had. If you add measurement after launch, the campaign has already lost strategic value. Step 4: Layer household impressions across channels. One postcard may get noticed, but repeated exposure builds recognition and intent. The practical target discussed was eight to twelve additional impressions connected to the original mail piece through digital follow-up, retargeting, email, landing pages, and device-level visibility. Step 5: Separate engagement from sales execution. A campaign can do its job by generating scans, calls, visits, form fills, or store traffic, while a weak website, poor phone handling, or uneven sales process can still lose the opportunity. This distinction protects strategic learning and prevents teams from blaming the wrong part of the funnel. Step 6: Use AI to keep improving the moving parts. AI can compare creative against historical campaign data, examine competitor mail, flag missing response mechanisms, and suggest changes to offer, timing, targeting, or layout. The goal is not magic; the goal is fewer guesses and better tests. From Blind Mail Drops to Performance Mail Systems Marketing Approach Primary Weakness AI-Enhanced Advantage Leadership Takeaway Traditional direct mail Limited attribution, slow feedback, and reliance on broad response assumptions Household-level engagement signals, personalized paths, and campaign-specific tracking Do not fund mail unless the measurement plan is designed before launch Digital-only acquisition Channel fatigue, rising competition, and dependence on rented platforms Physical mailbox presence reinforced by digital impressions and retargeting Use mail as a trust signal and a differentiator where digital attention is crowded AI-enabled performance mail Requires disciplined data, clean testing, and cross-functional follow-through Predictive creative review, competitive intelligence, attribution, and response lift modeling Treat direct mail as a system, not a single tactic Critical Questions for Leaders Testing Offline Channels What customer value threshold makes direct mail worth testing?  A practical starting point is a high-consideration product or service where the average order value gives the campaign enough margin to absorb print, postage, technology, and follow-up costs. How should a team judge success before revenue closes?  Track engagement rate through calls, scans, landing page visits, texts, website activity, and other attributable actions, then evaluate sales conversion as the next layer of accountability. Why is one mailing rarely enough?  Marketing response improves with repetition, and direct mail becomes stronger when the household receives multiple coordinated impressions before and after the physical piece arrives. Where does AI create the most practical advantage?  AI is useful when it audits creative, compares campaign patterns, studies category activity, identifies missing response paths, and helps the marketer decide what to change before the campaign is mailed. What testing mistake can mislead the executive team?  Sending one version first and another version later to the same households can create a memory effect, which makes the results less reliable. Clean audience splits and clear timing matter. Author: Emanuel Rose, Senior Marketing Executive, Strategic eMarketing Contact: https://www.linkedin.com/in/b2b-leadgeneration/ Last updated: Brad Kugler LinkedIn: https://www.linkedin.com/in/bradkugler/ dm20: https://dm20.com dm20.ai: https://dm20.ai Who’s Mailing What: https://www.whosmailingwhat.com Marketing in the Age of AI podcast: https://podcasts.apple.com/us/podcast/marketing-in-the-age-of-ai-with-emanuel-rose/id1741982484 About Strategic eMarketing: Strategic eMarketing helps growth-minded B2B and professional services teams build practical AI-enabled marketing systems that improve trust, lead quality, and revenue visibility. https://strategicemarketing.com/about https://www.linkedin.com/company/strategic-emarketing https://podcasts.apple.com/us/podcast/marketing-in-the-age-of-ai-with-emanuel-rose/id1741982484 https://open.spotify.com/show/2PC6zFnFpRVismFotbNoOo https://www.youtube.com/channel/UCaLAGQ5Y_OsaouGucY_dK3w Guest Spotlight Guest: Brad Kugler LinkedIn: https://www.linkedin.com/in/bradkugler/ Company: CEO and co-founder of dm20.com, dm20.ai, and Who’s Mailing What Podcast episode link: Not provided in source materials About the Host Emanuel Rose is a senior marketing executive and the host of Marketing in the Age of AI, where he helps leaders turn AI into practical advantage through clearer messaging, stronger trust, and smarter systems. Connect with Emanuel on LinkedIn at https://www.linkedin.com/in/b2b-leadgeneration/. Put the Mailbox Back Into the Measurement Plan If you are considering direct mail, don’t begin with a design file; begin with the business case, the audience, the offer, and the attribution plan. Then use AI to pressure-test the campaign before launch, measure engagement with discipline, and make the next drop smarter than the last one. Watch the podcast episode featuring Brad Kugler: https://youtu.be/pr7VWEfdogM