AI only becomes a competitive advantage when it is wired directly to revenue, disciplined testing, and better human management. The teams that win are not the ones using the most tools, but the ones turning prompts, prompts, and more prompts into clear rules, quantitative audits, and tighter leadership habits.
- Shift your economic model and mindset from “percentage of spend” to “percentage of incremental sales” so your incentives follow ROAS, not budget.
- Teach AI your rules before you ask it for recommendations; generic optimization logic tends to replicate the same mistakes weak agencies make.
- Use AI to run counterfactual performance audits (“what would have happened if…”) so you can sell and lead with hard numbers instead of subjective creative opinions.
- Accept that the final 10 percent of quality is where the real work — and the real value — sits; build human review and refinement into every AI-driven process.
- Treat AI outputs as training data for your people: use scored calls, annotated conversations, and “best-of” libraries to onboard and uplevel your team.
- Let AI also train you as a leader: the discipline of structured feedback to models should mirror the way you coach and reinforce performance with your staff.
- Start small but go deep: a single, well-crafted 30-page prompt attached to a critical workflow beats a dozen shallow experiments scattered across the organization.
The Samson–Rose Quant Loop: Turning AI Prompts Into a Pipeline
Step 1: Tie your economics to incremental revenue
Begin by aligning your agency or in-house team on ROAS and incremental sales rather than media spend. When fees are pegged to uplift rather than budget, everything that follows — testing, optimization, and AI use cases — orients around profitable growth, not just activity.
Step 2: Codify your rules before you automate
Document the decision logic you already trust: testing thresholds (for example, $200 test budgets), pass/fail criteria, acceptable ROAS bands, and scaling rules. AI works best as an amplifier of clear thinking; without those guardrails, it simply mirrors common industry mistakes at scale.
Step 3: Ask AI for counterfactuals, not just copy
Go beyond ad ideas and headlines. Feed your historical performance data into an agent and ask it to simulate what would have occurred had your rules been applied: which ads would have been killed, which scaled, and what the net ROAS impact would be. This is where audits move from opinion to quantification.
Step 4: Build dashboards, then scrutinize the last 10%
Turn those simulations and rules into living dashboards that your team can use daily. Expect AI to get you to about 90 percent quality quickly, then invest disproportionate human effort in the final 10 percent, where nuance, edge cases, and trust are won or lost.
Step 5: Instrument your conversations, not just your clicks
Attach transcription and a robust, multi-page scoring prompt to every important meeting. Quantify how client calls are run, where expectations are missed, and where relationships are strengthened. Use high-scoring calls as training assets for new account managers and as a mirror for your own communication behavior.
Step 6: Feed the feedback loop — for AI and humans
Close the loop by pushing your human-edited, high-quality outputs back into the models and giving your team similarly detailed feedback. Over time, the system learns what “great” looks like, while you evolve as a leader who coaches with clarity, specificity, and positive reinforcement.
From Yellow Pages Orphan To AI-Enabled Operator
Dimension | Old-School Agency Model | AI-Naive Automation | AI-Enabled Revenue Operator |
|---|---|---|---|
Economic Incentive | Paid on % of media spend; growth equals bigger budgets. | Paid on tools or licenses; success measured in usage. | Paid on incremental sales and ROAS; growth equals profitable scale. |
Use of AI | Minimal or cosmetic; occasional copy or audience ideas. | Let the model “optimize” accounts based on generic best practices. | The model is trained on your rules, thresholds, and business math before being unleashed. |
Human Leadership Role | Traffic manager between the client and channel specialists. | Hands-off: assumes AI will self-correct without strong oversight. | Designer of rules and feedback loops; manager of humans and agents in concert. |
Leadership Insights From The Noble Elements Of Group 8A
Question: How should a leader think about the risk that AI will eventually replace agencies or internal marketing teams?
The risk is real if your only value is pushing buttons on ad platforms, because those tasks will be compressed into tools. The antidote is to define your core as marketing expertise and human management: designing rules, making tradeoffs around risk and ROAS, and managing the people and agents who execute. As long as humans matter in shaping offers, stories, and relationships, there is room for a firm that knows how to orchestrate them.
What does “asking for bigger things” from AI look like in practical terms?
Instead of asking for surface outputs like “ten ad ideas,” push the model to do work that humans could not realistically complete: multi-scenario counterfactuals on a year of media spend, pipeline simulations under different ROAS thresholds, or 30-page call analyses that surface patterns across dozens of meetings. This reframes AI from a toy into a strategic analyst that unlocks decisions you were previously guessing at.
How can leaders avoid AI simply reinforcing bad, industry-standard behavior?
Do not hand over accounts to a model with vague prompts like “optimize this.” Instead, be explicit about what “good” is for your business: minimum viable test budgets, acceptable variance in ROAS, when to pull back spend, and how long to let a test run. Then monitor the outputs against those expectations. When AI drifts into the same errors you see from weak agencies — over-favoring high-spend, low-ROAS campaigns, for instance — correct it and bake that correction back into the prompt.
What is the leadership lesson inside the 30-page call-scoring prompt?
It shows that culture and quality can be operationalized. By defining what a “great client call” looks like and scoring every interaction, you turn something fuzzy into a training and management system. New account managers can binge-watch high-scoring calls, struggling ones can be coached against objective criteria, and you, as a leader, gain visibility into whether your standards are actually being lived, not just described in onboarding decks.
How does working with AI change the way a leader should give feedback to their team?
With AI, you quickly learn that detailed, specific reinforcement is what shapes behavior: “This section was strong because of X; this part needs more Y.” That same discipline should carry over to your people. When you see great work, say so and say why. When performance falls short, ground your feedback in the same clarity you use for prompts. Over time, your organization starts to operate on clear signals instead of vague impressions.
Author: Emanuel Rose, Senior Marketing Executive, Strategic eMarketing
Contact: https://www.linkedin.com/in/b2b-leadgeneration/
Last updated:
- Samson, E. Conversation on revenue-based agency models and ROAS-first scaling, “Marketing in the Age of AI” podcast.
- Rose, E. Authentic Marketing in the Age of AI, Amazon Publishing.
- Group 8A internal process descriptions on test budgets, ad pass/fail rules, and performance dashboards (as shared verbally).
- Fireflies.ai documentation on call transcription and meeting analytics (for structuring call-scoring workflows).
About Strategic eMarketing: Strategic eMarketing builds and manages data-informed, AI-assisted marketing systems for growth-minded organizations that want clear messaging, a better-qualified pipeline, and measurable ROI from every campaign.
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: Eric Samson
Title: Founder, Group 8A
Company: Group 8A – a NYC-based full-service digital marketing agency specializing in scalable, intent- and discovery-based marketing tactics.
LinkedIn: https://www.linkedin.com/in/ericsamson
Website: https://group8a.com
Podcast episode: Marketing in the Age of AI with Emanuel Rose featuring Eric Samson (Group 8A) — available via the Marketing in the Age of AI show feeds listed above.
About the Host
Emanuel Rose is a senior marketing executive and agency leader who helps organizations turn AI from a confusing add-on into a practical edge through clearer messaging, stronger trust, and smarter systems that move the revenue needle. He hosts the “Marketing in the Age of AI” podcast and consults with teams looking to blend human judgment with machine intelligence.
Connect with Emanuel on LinkedIn
From Theory To Practice: Your Next Three Moves
Choose one core workflow — media testing, client call reviews, or content refresh — and build a deep, specific prompt around it instead of dabbling in a dozen shallow use cases. Wire that workflow to a clear financial metric, such as ROAS or retention, and commit to a review cadence where you refine both the prompt and your team’s behavior based on results. Within a few cycles, you will have moved AI from novelty to an operating system for better decisions and stronger leadership.