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/emanuelrose
Last updated:
- OpenAI model and tooling documentation for building custom GPTs and agents
- Anthropic Claude documentation on tool use, safety, and autonomy patterns
- Industry research on prompt injection and LLM security risks in operational environments
- Vidar Hokstad’s public writing and LinkedIn posts on DevOps, AI, and startup execution
- Case observations from “Marketing in the Age of AI” client and listener implementations
About Strategic eMarketing: Strategic eMarketing helps B2B and mission-driven organizations design authentic, AI-aware marketing systems that generate qualified demand and long-term growth.
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: Vidar Hokstad
LinkedIn: https://www.linkedin.com/in/vhokstad/
Company: Hokstad Consulting (DevOps and AI consultancy, while bootstrapping his following product)
Episode: Marketing in the Age of AI – conversation on DevOps, AI agents, and how technical leaders turn AI work into tangible business outcomes.
About the Host
Emanuel Rose is a senior marketing executive and author of “Authentic Marketing in the Age of AI,” helping organizations integrate AI into practical, human-centered marketing systems. Connect with him on LinkedIn: https://www.linkedin.com/in/emanuelrose.
From Curiosity to Practice: Your Next AI Leadership Moves
Pick one friction-heavy workflow—proposals, reports, or meeting follow-up—and build a small project brain plus a custom GPT around it. Run three cycles: first draft by the model, your edited slice, then a refinement pass guided by your edits. As soon as you see a pattern that works, document it, share it with your team, and make it the new standard instead of a one-off experiment.