AI advertising only becomes a practical advantage when leaders stop treating creative as opinion and start treating each campaign as a learning system. The strongest takeaway from Misha Leybovich’s work with Adsmith.ai is simple: every ad is a guess, so the advantage goes to the team that can make more structured guesses, measure them cleanly, and reinvest based on signal.
- Replace creative debates with structured experimentation tied to performance data.
- Build campaigns around decision quality, not just asset production.
- Use AI to scale the number of valid tests your team can run without inflating labor costs.
- Separate creative inputs from targeting inputs so the model and the platform each get what they need.
- Favor aligned pricing and vendor models where performance creates mutual upside.
- Look for AI systems that learn from customer-specific data rather than relying on one-off prompts.
- Move toward a channel-neutral media strategy where budget allocation follows evidence, not platform bias.
The Guess-Test-Learn Advertising Loop
Step 1:
Start with the premise that no marketer knows with certainty which ad will work. That humility is not weakness; it is the foundation of disciplined marketing. When every ad is treated as a hypothesis, the team can stop defending opinions and start building evidence.
Step 2:
Break the campaign into decision components: audience, message, offer, creative concept, visual style, platform, geography, and budget allocation. AI becomes useful when those decisions are structured enough to generate, deploy, and evaluate at scale.
Step 3:
Create a high volume of controlled variations rather than betting the quarter on a small number of polished assets. The goal is not random activity; the goal is more shots on goal with enough structure to learn from the outcomes.
Step 4:
Connect each experiment to clean performance feedback from the ad platforms. Systems that send assets out through APIs and receive performance data back can begin to form a learning loop that humans alone cannot maintain at the same pace.
Step 5:
Separate noise from signal before scaling spend. A few failed tests are not a problem if they are inexpensive and informative. The real value appears when the winning patterns reveal which decisions are worth repeating.
Step 6:
Reinvest based on evidence across campaigns, customers, and channels while respecting the context of each brand. A B2B software company, a local service business, and a consumer brand should not be blended blindly, but their structured performance data can still improve decision quality over time.
Agency Intuition Versus AI-Driven Experimentation
Dimension | Traditional Agency Pattern | AI Experimentation Pattern | Leadership Takeaway |
|---|---|---|---|
Creative development | Relies heavily on expert opinion, limited rounds, and subjective approval. | Generates many structured variants and lets performance data identify stronger directions. | Shift the role of leadership from approving taste to approving the testing architecture. |
Measurement discipline | Often, reviews of outcomes after campaigns have already consumed a meaningful budget. | Continuously collects platform feedback and uses it to guide the next set of decisions. | Build measurement into the operating model before increasing spend. |
Channel allocation | It may be shaped by agency habits, platform familiarity, or siloed reporting. | Can move toward channel-neutral budget allocation where evidence drives rebalancing. | Choose partners and systems that can act as a fiduciary for the advertiser, not the platform. |
Five Strategic Questions Leaders Should Ask Before Scaling AI Ads
Are we asking AI to make ads, or are we asking it to improve decisions?
Asset generation is now the easy part. The competitive edge comes from knowing what to generate, why it should exist, which audience should see it, and what the results teach us.
Do our systems capture the decisions behind each campaign?
If the only data you have is the finished ad and the outcome, you are missing the causal trail. Leaders need the inputs, assumptions, creative variables, and campaign structure stored in a way that can be analyzed later.
Are we overpaying for human processes where software can remove friction?
Smaller companies often need the benefits of sophisticated ad operations without the cost structure of a full agency team. AI-supported systems can make agency-level execution more accessible when the workflow is designed well.
Is our vendor aligned with our success?
A percentage-of-spend model is imperfect, but it can be closer to aligned incentives than flat retainers when paired with transparent reporting. The deeper goal is to measure the profit created by advertising, not just the money spent on media.
Are we using each platform’s tools, or are we letting each platform define our strategy?
Google and Meta will optimize for their own ecosystems. A serious marketing operation needs a neutral layer that can compare channels and place budget where the customer’s data supports it.
Author: Emanuel Rose, Senior Marketing Executive, Strategic eMarketing
Contact: https://www.linkedin.com/in/b2b-leadgeneration/
Last updated:
- Conversation transcript: Marketing in the Age of AI with Emanuel Rose and Misha Leybovich.
- Guest notes supplied for Misha Leybovich and Adsmith.ai.
- Adsmith.ai: https://adsmith.ai
- Misha Leybovich LinkedIn: https://www.linkedin.com/in/mishaley/
About Strategic eMarketing: Strategic eMarketing helps B2B leaders strengthen trust, clarify messaging, and apply AI-enabled marketing systems 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: Misha Leybovich
LinkedIn: https://www.linkedin.com/in/mishaley/
Company: Adsmith.ai
Podcast episode link: Not provided in the source materials.
Guest email: misha@adsmith.ai
Misha Leybovich has been building marketing tools for 15 years. He is the founder of Adsmith.ai, an AI ad agency focused on statistical experimentation, learning, and performance improvement. His background includes building AI products at Google Labs, selling Starlink at SpaceX, and roles at McKinsey, MIT, Berkeley, and Cambridge.
About the Host
Emanuel Rose is a senior marketing strategist, author, and host of Marketing in the Age of AI. He helps business leaders turn AI from a confusing add-on into a practical advantage through clearer messaging, stronger trust, and smarter systems. LinkedIn: https://www.linkedin.com/in/b2b-leadgeneration/
Put the Learning System to Work This Week
Choose one active campaign and identify the key decisions behind it: message, audience, creative direction, offer, channel, and budget. Then create a small batch of structured tests, define the learning goal before launch, and let the data tell you what deserves more investment.
The leaders who win with AI will not be the ones who generate the most content. They will be the ones who build systems that learn faster than their competitors.
Watch the podcast episode featuring Misha Leybovich: https://youtu.be/op83MBUhEgk