AI is no longer a side project; it’s a leadership discipline that reshapes how you work, how customers discover you, and how you turn your personal expertise into scalable assets. The organizations that win will treat AI as a hands-on practice—experimenting, building micro-tools, and rewiring marketing and innovation workflows around them.
- Stop waiting for “the big AI project” and start running many small, fast experiments that change how your team works this quarter.
- Redesign marketing workflows so AI handles research, drafting, and repurposing while humans focus on judgment, relationships, and narrative.
- Move beyond SEO into generative engine optimization (GEO), so AI assistants and answer engines can actually find, trust, and recommend your brand.
- Use AI to create deep, long-tail, persona-specific content clusters that map to how real people ask real questions.
- Turn your own service expertise into software-like tools that save hours internally and can evolve into new offerings.
- Treat AI tools like new team members: give clear briefs, iterate, and refine, rather than quitting after the first imperfect output.
- Protect and grow the value of real-world relationships and live events as a differentiator in an increasingly automated communication environment.
The Think Big AI Loop: A 6-Step Leadership Cycle
Step 1: Acknowledge the disruption at the top
AI is restructuring how value is created, discovered, and delivered. Leaders who “sit on the fence” signal to their teams that experimentation is optional. The first move is a clear, visible commitment from leadership that AI adoption is strategic and non-negotiable.
Step 2: Map where AI can change how you work
Before chasing shiny tools, identify the flows that actually drive your marketing and innovation engine: research, content, campaigns, customer insights, and collaboration with sales and service. Document them. Then target the ones with the highest manual drag and the clearest outcomes.
Step 3: Build micro-tools, not monoliths
Follow Amir’s approach: use vibe coding platforms and custom AI agents to build small, single-purpose tools that save hours—proposal generators, RFP assistants, content repurposers, and FAQ builders. These lightweight apps deliver value fast and teach your teams how to think and build with AI.
Step 4: Industrialize long-tail, AI-ready content
Shift from generic blog posts to structured, question-driven content that speaks to specific personas and situations. Use AI to mine Reddit, YouTube, Discord, and customer dialogues for fundamental questions, then generate deep, 2,000+-word answers and FAQ hubs that answer engines can confidently surface.
Step 5: Optimize for AI discovery, not just search engines
Answer engines and AI assistants read and rank content differently than humans. Ensure your pages load complete answers (not just lazy-loaded fragments), are up to date, and are structured so models can quickly detect relevance. Think in terms of “Would an AI agent pick this as the safest, clearest recommendation?”
Step 6: Close the loop with experimentation and refinement
Treat every AI tool and content asset as a live experiment. Track traffic, conversion, and time savings. When a workflow or tool underperforms, refine your prompts and requirements the way you’d coach a new hire, instead of declaring “AI doesn’t work.” This loop—commit, map, build, publish, observe, refine—keeps you learning faster than competitors.
From Lagging to Leading: AI Marketing Adoption Compared
Dimension | Laggard Organizations | AI-Experimenting Teams | AI-First Leaders |
|---|---|---|---|
Leadership stance on AI | Sees AI as a risk or optional add-on; no clear mandate | Supports pilots but treats them as side projects | Declares AI core to strategy and personally sponsors initiatives |
Marketing workflows | Manual research, one-off content, slow approvals | Uses generic tools (chatbots, basic copy) without process change | Redesigned flows so AI handles research, drafting, and repurposing at scale |
Discoverability in AI channels | SEO-only mindset; old content rarely refreshed | Occasional updates; no structured GEO plan | Systematic long-tail content, FAQs, and structured pages for answer engines |
Field Notes from the AI Frontline: Leadership Q&A
How should a mid-level marketing leader influence an executive team that underestimates AI?
Start by reframing AI from a “tech experiment” to a revenue and relevance issue. Bring concrete examples: a before/after case where AI tools cut proposal time from hours to minutes, or where GEO-driven content lifted qualified traffic. Propose one low-risk, high-visibility pilot tied to a metric your executives already care about—pipeline velocity, lead quality, or campaign cycle time—and commit to reporting back in 30–60 days.
What are the first workflows a marketing team should automate or augment with AI?
Repetitive target activities, text-heavy, and currently bottlenecked. Good starting points include market and persona research, drafting long-form content, repurposing podcasts and webinars into articles and social posts, and building structured FAQ content. These are areas where tools like Claude, custom GPTs, and lightweight internal apps deliver fast wins without touching core systems.
How does generative engine optimization differ from classic SEO in practice?
Traditional SEO optimizes for humans scanning results pages; GEO optimizes for AI systems that read full pages, synthesize answers, and then recommend. Practically, that means fresher content (frequent updates to avoid being ignored), full-page load without critical info hidden behind lazy loading, clearly structured answers to specific questions, and dense, trustworthy explanations that models can confidently quote or summarize.
What is the practical value of turning services into small AI-powered products?
When you “software-ize” your expertise—like Amir’s campaign ideation app or a proposal generator—you gain three advantages: you save your own time, you standardize quality, and you create the option to offer those tools externally. Even if a tool never becomes a product, it becomes an asset that lets you serve more clients or run more campaigns with the exact headcount.
How can teams avoid giving up too quickly when AI outputs disappoint?
Adopt the “new hire” mindset Amir described. Assume the first output is a rough draft, not a verdict on the tool’s value. Clarify the brief, give examples of good and bad output, and iterate. Document effective prompts and workflows so the team doesn’t have to start from scratch every time. Over a handful of cycles, quality typically jumps from “usable with heavy edits” to “95% done,” which is where the real leverage lives.
Author: Emanuel Rose, Senior Marketing Executive, Strategic eMarketing
Contact: https://www.linkedin.com/in/emanuelrose
Last updated:
- Elion, Amir – Conversations on AI-led innovation, Think Big Leaders materials, and public talks.
- Rose, Emanuel – Authentic Marketing in the Age of AI, Amazon Publishing.
- Industry practice observations from client work across B2B technology, healthcare, and manufacturing sectors.
- Public documentation and release notes from leading AI platforms (OpenAI, Anthropic, Google, and primary vibe coding tools).
About Strategic eMarketing: Strategic eMarketing helps growth-minded organizations design and execute measurable marketing systems that blend AI, storytelling, and data-driven execution to generate a consistent pipeline and revenue.
https://strategicemarketing.com/about
https://www.linkedin.com/company/strategic-emarketing
https://podcasts.apple.com/us/podcast/marketing-in-the-age-of-ai
https://open.spotify.com/show/marketing-in-the-age-of-ai
https://www.youtube.com/@EmanuelRose
Guest Spotlight
Guest: Amir Elion, Founder & Senior Advisor, Think Big Leaders; Co-founder, Global Green Action Day
LinkedIn: https://linkedin.com/in/amirelion
Email: amir.elion@gmail.com
About Amir: Amir is an experienced professional in business, innovation, and transformation. He founded Think Big Leaders and co-founded Global Green Action Day. Previously, he led the Digital Innovation program at Amazon Web Services in the Nordics, helping customers design and launch new products and customer experiences. His background spans Motorola Solutions, Teva Pharmaceuticals, two startups where he served as Director of Products, and strategy and innovation consulting. Amir has worked with companies such as Novo Nordisk, Volvo Group, Adobe, Johnson & Johnson, and Booking.com, and is a recognized author and keynote speaker on innovation methodologies and technologies like generative AI and cloud. He has created online courses with thousands of students, frequently speaks at international conferences, and has lectured at various institutions. He holds AWS Solutions Architect Associate, AWS Certified AI Practitioner Early Adopter, and Microsoft Azure AI Fundamentals certifications, leads the AI Thematic group at Sweden’s Innovation Leaders Association, and previously chaired the national Israeli learning and development conference.
Company: Think Big Leaders – Advisory and innovation partner for organizations seeking to harness AI and structured innovation mechanisms.
Episode recording date & time: Mon, Dec 8th, 2025, at 7:30 AM PST (America/Los_Angeles) / 4:30 PM CET (Europe/Stockholm)
Podcast: Marketing in the Age of AI with Emanuel Rose
Episode link: Visit the Marketing in the Age of AI feeds on Apple Podcasts, Spotify, or YouTube around the Dec 8, 2025, release window to access this conversation with Amir Elion.
About the Host
Emanuel Rose is a senior marketing executive and author of “Authentic Marketing in the Age of AI,” specializing in performance-driven campaigns that blend human storytelling with intelligent automation. He leads Strategic eMarketing and hosts the “Marketing in the Age of AI” podcast.
LinkedIn: https://www.linkedin.com/in/emanuelrose
Turning Insight into Action: Your Next 7 Days with AI
Pick one marketing workflow and one discoverability challenge and apply the Think Big AI Loop: map the process, design a small AI-powered tool or prompt system, publish at least one long-tail content asset, and measure the impact. Use the results as your proof point to bring leadership deeper into the conversation and to fuel the next round of experiments.
The leaders who move now—building internal tools, GEO-ready content, and AI-literate teams—will own the compound returns that follow.