Emanuel Rose

Hiring remains one of the most strategic levers in any business, but most teams are relying on email, spreadsheets, and guesswork. Sean Griffith’s work with Truffle demonstrates how to combine AI, one-way video, and disciplined SEO to build a scalable, human, and data-driven hiring engine. Stop treating hiring as an ad hoc fire drill and design a repeatable, intelligence-driven process that compounds over time. Use AI to screen, rank, and structure interviews, but keep humans in control of the conversation and final decisions. Shift the content strategy toward mid- and bottom-of-funnel intent, cluster it tightly around your ICP and use cases, and use it to drive conversion. Exploit “generative engine optimization” early by seeding LLMs with differentiated content and positioning. Adopt a product-led growth motion with low-friction entry pricing, then systematize expansion and upsell. Build shared, custom AI tools (such as GPTs) within your company to standardize roadmaps, messaging, and execution. Think of hiring as a feedback loop: feed post-hire performance data back into your models and interview design. The Hiring Intelligence Loop: A 6-Step Truffle-Inspired Playbook Step 1: Treat hiring as a core product, not a back-office function Sean’s experience at SimpleTexting and Truffle underlines a simple truth: the people you bring in determine how fast and how well you can execute. Most leaders wait until “the house is on fire” before hiring, then rush through resumes and make gut decisions. Reframe hiring as a product you’re constantly improving—with clear workflows, metrics, and ownership. Step 2: Replace resume roulette with structured one-way interviews Resumes have become unreliable signals, especially with AI-written profiles and keyword stuffing. Truffle’s starting point is a one-way video interview that lets candidates respond to standardized questions asynchronously. This removes scheduling bottlenecks and provides a much richer signal about communication, thinking, and culture fit than a PDF ever will. Step 3: Layer AI on top of human-designed questions—not the other way around Truffle doesn’t hand the interview over to a synthetic avatar; it lets real hiring teams record or write the questions and then uses AI to analyze responses. The AI sorts and ranks candidates against job requirements and culture markers while surfacing a short list worth your time. Humans still define what “good” looks like and decide who moves forward. Step 4: Build a PLG funnel that starts light and expands with value Sean uses a product-led play: make it easy for a skeptical small business or team to start on a modest plan, prove value quickly, then expand usage. Many Truffle customers start on the lowest tier, move to the mid-tier a month later, and upgrade to larger plans as they roll the tool across locations or departments. Pricing, onboarding, and education are all designed to make that journey natural. Step 5: Connect hiring signals across the full lifecycle The future Sean is building toward is “hiring intelligence,” not just interviewing. That means stitching together resumes, one-way interviews, live interviews, and post-hire performance. When you can say, “Here’s what our successful hires looked like at the application and interview stage,” your next job posting, screening, and questioning can be tuned for much higher hit rates. Step 6: Feed AI with your own data, positioning, and systems Internally, Sean’s team uses custom GPT-style agents and shared projects to enforce consistent roadmaps and positioning. They keep their product messaging and strategy loaded into their AI assistants so that every content piece and roadmap artifact aligns. That same principle applies to hiring: the more your tools “know” your culture, ICP, and success patterns, the more leverage you get from AI. From SEO to AEO: How Truffle’s Strategy Differs from Old-School Hiring Tools Dimension Traditional SMB Hiring Truffle’s AI-Powered Approach Strategic Impact for Leaders Screening Process Manual resume review, ad hoc phone screens, limited to a handful of candidates due to time constraints. Asynchronous one-way video interviews with AI-assisted ranking across large applicant volumes. Leaders can evaluate far more candidates without burning out the team, improving the odds of high-quality hires. Use of AI Occasional keyword filters in ATS; minimal intelligence and no context about culture or role nuance. AI analyzes responses, matches candidates to role and culture, and flags potential AI-generated submissions. AI becomes a signal amplifier, not a gatekeeper, helping humans make sharper, faster hiring decisions. Go-to-Market & Growth Sales-heavy motion, little content depth, and almost no presence in generative search environments. Self-serve PLG model, deep SEO with content clusters, and early investment in generative engine optimization. Reduced CAC, stronger inbound pipeline, and early advantage in AI-driven discovery channels. Five Strategic Questions Leaders Should Be Asking After Sean’s Playbook How many qualified candidates are we missing because our current process doesn’t scale? If your team caps out at a few dozen resume reviews per role, you’re leaving talent on the table. Truffle’s customers routinely deal with hundreds or even thousands of applicants. By using one-way interviews and AI-assisted ranking, they can screen far more people without adding headcount. The key question is: what would your hiring outcomes look like if you could reliably evaluate 5–10 times more candidates per role? Where is AI best suited in our hiring funnel—and where should humans stay front and center? Sean draws a clear line: AI should augment screening and analysis, not impersonate interviewers. AI excels at ranking, clustering, and pattern recognition—tasks such as detecting likely culture fit, comparing responses against desired competencies, and flagging red flags. Human leaders should remain responsible for designing questions, conducting live interviews with finalists, and making final calls. Use AI where scale and pattern-matching matter; use humans where nuance, trust, and context matter. Are we still doing “SEO from 2018,” or are we adapting to how people actually search with LLMs? Sean still leans heavily on quality content, but he’s shifted focus to mid- and bottom-of-funnel queries and builds tight content clusters around specific ICPs. Additionally, he deliberately optimized for generative search—securing Truffle early mentions in tools like ChatGPT. Even though algorithms and weightings have shifted, the takeaway remains: you need a strategy

Technical leaders don’t need to become full-time marketers to win with AI. They do need disciplined systems: narrow problem definitions, tightly scoped agents, and feedback loops that turn one good outcome into a reusable playbook. Stop treating AI as magic; treat it as a junior partner that works from clear documents, constraints, and examples. Build project “brains”: small folders of instructions, prior work, and reference docs that every agent or model must use. Partition risk: give each agent an ultra-specific job and only the minimum data and permissions needed to do it. Use AI to critique your messaging, not just to write it; ask it to poke holes in your offer, niche, and positioning. Capture your best AI runs by asking the model to summarize what worked into reusable operating instructions. Start with low-risk, high-friction tasks (proposals, reports, meeting summaries) to earn fast ROI and free up human focus. Assume prompt injection is a real security threat any time an agent touches email, calendars, bug trackers, or internal tools. The Hokstad Loop: A Six-Step System for Productive AI Work Define the smallest beneficial outcome Before you open any tool, decide on a concrete, narrow deliverable: a labeled inbox, a draft proposal, a cleaned-up report, a prioritized backlog. Vague goals (“improve sales” or “optimize costs”) lead directly to vague output and wasted cycles. Assemble a project brain Create a small repository (folder, project, or custom GPT knowledge base) with three elements: instructions (how you want the work done), examples (good and bad), and facts (verified data about the project, client, or system). Every agent run pulls from this same “brain.” Let the model take the first pass Feed the model your project brain and let it generate the first draft: report, proposal, presentation, lead list, or classification. Don’t chase perfection; you’re buying speed and structure, not a finished product. Edit a slice, then teach it back Manually refine a small, representative section—tone, risk language, structure, or prioritization. Then ask the model to analyze your edits, extract the patterns, and apply them across the rest of the output. This is how you turn one good slice into a consistent result. Capture the “how,” not just the “what” When a run works well, ask the model to summarize the process as reusable instructions: prompts, constraints, and checks that contributed to the outcome. Save that summary into your  Tighten scope and permissions before scaling Only after several safe, successful runs should you connect agents to live systems (email, calendar, bug tracker, CI/CD). Even then, split capabilities: one agent that classifies, another that drafts responses, a third that proposes changes—but none with blanket access and authority. When to Use Custom GPTs vs. Autonomous Agents vs. Point Tools Approach Best Use Cases Key Advantages Primary Risks / Limitations Custom GPTs / configured chatbots Content creation, proposals, reports, structured thinking, critiquing messaging, guided analysis Low setup friction; safer sandbox; easy to align with your tone, domain docs, and preferred workflows Limited autonomy; requires human orchestration; can’t safely act on live systems without extra plumbing Autonomous or semi-autonomous agents Ongoing classifications (e.g., email labeling), repetitive operational tasks, background data preparation, DevOps automation Runs while you work on other things; can chain tools; powerful leverage when tightly scoped Severe security exposure if over-permissioned; prompt injection; harder for nontechnical leaders to design safely Specialized AI-powered tools Presentations from text, design-ready proposals, niche workflows (e.g., slide builders, video editors) Fast time-to-value; opinionated UX; often very close to “ready to ship” outputs from minimal input Can be displaced as base models improve; less flexible; risk of locking crucial workflows into closed platforms Leadership Insights from a Tech Founder Turned AI Operator How should a technical founder think about marketing when it’s not their natural strength? Treat marketing like engineering. Start by defining the problem in narrow terms: “I need ten qualified conversations per month” is more useful than “I need more leads.” Use AI to explore positioning, test different niches, and critique your messaging. Then pick one ICP and one core problem, and build a simple repeatable funnel—lead magnet, outreach script, and follow-up sequence—before you worry about complex campaigns. What’s a practical way to evaluate whether an AI project will create real ROI? Ask three questions: Does this reduce a measurable cost today (time, infrastructure, errors)? Can we implement it within existing workflows without changing how everyone works? Can we ship a test in weeks, not quarters? If the answer is “no” to any of these, keep it in the experimental bucket and don’t sell it as a core initiative yet. How do you keep your AI usage sharp when the ecosystem moves so quickly? Build learning into client work. Every engagement becomes both delivery and research: you’re evaluating new models, tools, and patterns as you solve concrete problems. If you’re hands-on—writing prompts, wiring agents, watching failures—you stay close enough to the ground that even a few weeks away doesn’t leave you completely behind. What’s the safest entry point for leaders who want agents but worry about security? Start with agents that can only classify or draft, never send or execute. An email classifier that applies labels is low-risk: the worst outcome is a marketing email marked urgent. A draft-response agent that can’t see old threads or pull arbitrary data is another safe pattern. Only move to read–write access when you’ve proven the behavior, split responsibilities, and can describe precisely what the agent can and cannot touch. How can teams turn one successful AI workflow into a durable advantage? After a win—say, a strong proposal generator or a reliable reporting pipeline—pause and institutionalize it. Capture prompts, instructions, examples, and edge cases into a shared repository. Pair that with a short “how we use this” guide and a few recorded walkthroughs. The leverage doesn’t come from a clever one-off; it comes from making that pattern the default way your team works. Author: Emanuel Rose, Senior Marketing Executive, Strategic eMarketing Contact: https://www.linkedin.com/in/b2b-leadgeneration/ Last updated: OpenAI model and tooling documentation for building custom GPTs

AI only creates value when leaders treat it as a managed transformation, not a magic trick or a forbidden toy. The organizations that win will assign clear ownership, invest in education and governance, and double down on the human soul of their brand and creativity. Stop “rolling out a chatbot” and start with a clear blueprint tied to cost, revenue, and risk. Assign one accountable AI leader (title aside) with authority across data, IT, operations, and change management. Invest in basic AI literacy for executives and teams so terms like “workflows” and “agents” have shared meaning. Create guardrails rather than bans to prevent shadow IT and uncontrolled data leakage. Use AI to augment—not replace—the human soul in your marketing and creative work. Adopt an “AI-first, human-in-the-loop” workflow for everyday tasks to build muscle memory. Start with small, visible pilots that actually touch your data and standard operating procedures. The CAIO Loop: A 6-Step Leadership Model for Real AI Adoption Name the Owner of the Ball Someone in your organization must “have the ball” for AI. Whether you call them Chief AI Officer, VP of AI, or Director of Intelligent Systems, the role needs explicit authority to coordinate across the CIO, CTO, data, security, and operations teams. Without this, AI decisions get stuck in committees and turf wars. Educate the Executive Bench Before you talk tools, align on language. Make sure your leadership team can explain, in plain terms, concepts like AI workflows, agentic systems, and data governance. This shared vocabulary is the foundation for realistic expectations and wise investment decisions. Map SOPs, Not Just Tech Stacks AI transformation is as much about standard operating procedures as it is about models and APIs. Have your AI leader work with operations to map how work actually gets done, where handoffs break down, and where machine intelligence could compress cycle time or eliminate drudgery. Connect AI to Your Data, Not Just Licenses Buying site licenses for a foundation model without connecting it to your systems and data is a glorified toy rollout. Design secure pathways between your core data stores and AI tools, with clear access rules and logging, so the technology can actually act on your context. Build Guardrails to Prevent Shadow IT Total bans do not stop AI; they just push it underground. Create a governance framework that defines what data can be used, which tools are approved, and how outputs are reviewed. That structure keeps your people experimenting without putting customer or corporate data at risk. Ship Small, Human-Centered Pilots Start with contained use cases where AI can demonstrably reduce cost or time—such as campaign drafting, research, or internal knowledge retrieval. Keep humans firmly in the loop, measure impact, and use each pilot to refine both your governance and your team’s intuition about what “good” looks like. CIO, CTO, CAIO: Who Owns What in AI Transformation? Role Primary Focus Core AI Responsibility Risk if Misaligned CIO Buying, implementing, and maintaining enterprise IT systems Ensure infrastructure, data platforms, and security posture can support AI workloads and compliant data access AI tools stay disconnected from core systems; governance gaps create security and compliance exposure CTO Building technology products and custom software Embed AI capabilities into products, platforms, and custom apps in ways that serve customers and internal users AI remains an isolated “labs” effort, never fully productized or aligned with business value CAIO (or equivalent) End-to-end AI strategy, change management, and value realization Own cross-functional roadmap, AI literacy, SOP redesign, and alignment between data, tech, and business outcomes No one has the ball; decisions stall in committees, and shadow projects proliferate without standards   From Pixels to Performance: Deep-Dive Insights on AI, Music, and Leadership How should leaders rethink “AI deployment” so it actually delivers ROI rather than becoming a corporate toy? Treat AI initiatives like any serious transformation: start from business outcomes, not from tools. Jason’s on-the-ground experience shows that buying licenses and “making it available” without training, data connections, or governance simply guarantees low adoption. A better approach is to pick a few clear problems—such as reducing campaign production time, speeding up analytics, or improving customer response quality—then design AI workflows around real SOPs with accountable owners, metrics, and change management baked in Why is assigning a single accountable AI leader so critical even in organizations with mature CIO and CTO functions? In large enterprises, AI cuts across every existing technology and data role: CIO, CTO, CISO, CDO, and digital leadership. Without a clearly designated owner, AI becomes a political football—everyone is touching it, but no one carries it into the end zone. Jason observes AI “consortiums” of five to seven executives that slow decisions and dilute accountability. By explicitly giving one person the AI hat—regardless of formal title—you create a focal point for strategy, prioritization, standards, and communication. What does the “uncanny valley” of AI-composed music teach marketers about AI-generated content? In the AI in A Minor project, classically trained musicians could feel that the compositions mimicked Mozart or Philip Glass, yet they did not truly understand them. Technically, the pieces were impressive, yet something in the emotional arc was off. That same gap exists in AI-written copy, visuals, and video: they can be structurally correct while lacking authentic voice, lived experience, or a coherent “soul.” For marketers, the lesson is clear—use AI to draft, ideate, and adapt, but let humans bring story, tension, and emotional truth that differentiates your brand from generic output. How does clamping down on AI usage backfire inside organizations? When leaders block AI tools across networks out of fear, they do not eliminate usage; they drive it underground. Jason sees this regularly: employees turn to personal devices and unvetted platforms, often pasting confidential data into consumer tools with no oversight. The organization loses visibility, increases risk, and misses the chance to guide best practices. A smarter path is to acknowledge that experimentation is already happening, then provide approved tools, clear instructions, and training so people can use AI safely and effectively. What is

AI automation is no longer about tools; it’s about designing systems that eliminate busywork, keep a human in the loop, and give owners back the hours they need for strategy, family, and craft. Bradford Carlton’s approach shows how non-technical founders can architect serious automations—end-to-end outreach, content engines, and reporting—by thinking like a lawyer and building like a process designer. Start from outcomes and workflows, not from tools; map the process, then choose the tech. Use an “AI-first” mentality to remove 4–6 hours of low-value work per week through transcripts, custom GPTs, and automated reports. Keep a human in the loop at every critical decision point; AI drafts, you approve and direct. Think in reusable systems: one automation for outreach, one for proposals, one for content, all feeding each other. Leverage orchestration tools (like n8n) and APIs to connect best-of-breed AI models, instead of waiting for a perfect all-in-one platform. Invest time up front building personal playbooks and SOPs so your AI “team” reflects your standards, not generic internet advice. Use the time you free up intentionally—for family, creative work, or deeper learning—not just more grind. The AI-First Workflow Loop for Owners Who Aren’t Coders Define the work you never want to do again Before opening any app, make a list of tasks that drain you: proposal writing, post-meeting summaries, manual prospecting, repetitive reports. Be specific about what “done” looks like for each. This becomes the spec for your automations and keeps you focused on real leverage rather than shiny features. Map the process in plain language. For each target task, sketch the sequence: inputs, decisions, actions, outputs. Bradford thinks like an attorney here—step-by-step logic, no code. “If X, then Y” on paper becomes the backbone for your workflows later, whether you’re using n8n, Replit, or another orchestrator. Turn AI into a drafting engine, not an autopilot. Use tools like ChatGPT, custom GPTs, or Claude to draft proposals from transcripts, emails from lead sheets, and content from book chapters. The AI creates the first version; your standards drive the edits. This reframes AI from replacement to multiplier. Orchestrate with automation, keep humans in the loop Once you trust the drafts, connect the pieces with an automation layer. Bradford uses n8n and HTTP nodes to talk directly to APIs—scraping leads, enriching data, triggering email sequences, and generating reports. Critically, every significant step pauses for a human decision: selecting leads, approving messaging, and confirming outreach. Harden the system through tests and bug-hunting Real automation is less about the first build and more about debugging. Bradford runs hundreds of records through his workflows, finds edge cases, and fixes logic breaks. Expect two weeks of “it keeps breaking” before you get to “this runs while I sleep.” Testing is where hobby projects become business infrastructure. Redeploy the time you win on what actually matters When you claw back 4–6 hours a week, decide in advance how that time will be used. Bradford leans into family, teaching, and building higher-level systems; I lean into nature, music, and deeper strategy. If you don’t allocate this time with intention, your calendar will refill with the same noise you just escaped. Choosing Your Automation Path: Coaching, Tools, or DIY Experiments Approach Who It’s For Key Advantage Main Risk Guided automation with a coach/consultant Owners who want systems built around their business model without becoming “the tech person.” Faster path to working automations, plus strategy and accountability layered on top. Becoming dependent on the expert if you don’t also learn the underlying logic. Tool-focused experimentation (n8n, Replit, Submagic, etc.) Tinkerers are comfortable learning one platform deeply and wiring services together. High control and customization; you can connect anything with an API and make it yours. Time sink and frustration if you skip process mapping and jump straight into building. Lightweight AI usage inside existing apps Very busy founders needing quick wins: transcripts, custom GPTs, basic content, and reports. Immediate time savings with minimal setup; doesn’t require understanding automation stacks. Hitting a ceiling quickly and leaving significant efficiency gains on the table. Field Notes from Building an AI-First Small Business Where should a non-technical owner begin with AI automation? Start with one workflow you already understand deeply and hate doing. A perfect entry point is post-meeting work: record your sales call or consult, have it transcribed, then run the transcript through a custom GPT or well-crafted prompt that outputs a proposal, summary, and next steps. You’ll feel the shift from “this takes me four hours” to “this takes me ten minutes,” and that experience will fuel your willingness to map and automate the following process. How do you keep control of brand voice when AI is drafting content and emails? Treat brand voice as a system prompt, not a vibe. Document tone, structure, phrases you use and avoid, and examples of “this sounds like us” versus “this does not.” Train your models and custom GPTs on that, and always insert a review step before anything is published or sent. Bradford’s systems generate email sequences and social posts, but they don’t go live until a human sees them. That small checkpoint keeps your reputation intact while still harvesting the time savings. What separates a clever automation from a true business asset? A clever automation saves a little time once; a business asset runs reliably at scale, on demand, and is documented well enough that someone else can maintain it. Bradford’s end-to-end outreach system—scraping, enrichment, fit analysis, sequencing, and social tracking—is an asset because it’s tested, debuggable, and integrated into the revenue-generating process. If your automation disappeared tomorrow and your revenue wouldn’t budge, you’ve built a toy, not a system. How do you balance AI power with the ethical need for real human contact? Draw a clear line between automation that supports relationships and automation that pretends to be the relationship. Use AI to research prospects, warm up leads with relevant content, summarize calls, and follow up with drafts. But show up for the conversations where trust is formed, and decisions are