Marketing R&D Model
Invest in experiments that turn AI learning into proprietary marketing capability.
- Difficulty
- Advanced
- Time to result
- ~months to results
- Steps
- 8
- Confidence
- 94%
The Marketing R&D Model treats AI-enabled workflows as proprietary capabilities that must be researched, tested, and developed rather than adopted through a few prompts. Leaders first identify a valuable production constraint and estimate what the organization could gain by solving it. They then protect time for learning and experiments, accepting that some efforts will fail. Success is measured through production-level output, shorter cycles, wider team access, and business impact—not by how many employees use an AI chatbot. Effective experiments are converted into reusable systems, while failed attempts reveal the next problem to investigate. This creates an accumulating capability base that can absorb improved models and tools without forcing the organization to restart whenever technology changes.
Origin
Extracted from Marketing Against The Grain during the discussion of why creative and marketing teams must fund AI workflow development like technology companies fund R&D.
Core principles
- 01Treat AI capability development as R&D rather than instant productivity software.
- 02Accept that some experiments will not produce useful results.
- 03Judge adoption by production impact, not tool usage.
- 04Develop proprietary processes around the organization's real needs.
- 05Fund the learning curve before expecting speed.
- 06Continuously raise capability as market expectations increase.
How to run it
- 1
Define the capability gap
Name a concrete production capability the team needs, such as rapid thumbnail testing or consistent campaign imagery. Describe the business constraint it would remove.
Pro tip Frame the opportunity as a repeatable capability, not access to a particular model.
Watch out Buying tools without selecting a capability usually produces scattered experimentation.
- 2
Estimate strategic value
Assess the frequency, cost, quality impact, and potential scale of solving the problem. Use that estimate to decide how much experimentation is justified.
Pro tip Prioritize capabilities that improve both speed and production quality.
Watch out Do not automate a low-value task merely because it is technically interesting.
- 3
Protect an R&D allocation
Set aside time, people, and budget that are not consumed by immediate delivery demands. Explicitly permit learning and unsuccessful trials.
Pro tip Separate tomorrow's urgent production work from capability-building time.
Watch out Trying to experiment inside every urgent deadline can make both the delivery and the research fail.
- 4
Run bounded experiments
Test one workflow assumption at a time against clear output criteria. Compare the generated result with the team's actual production standard.
Pro tip Keep a baseline from the current process so improvement can be demonstrated.
Watch out A compelling demo is not evidence that a workflow works consistently.
- 5
Diagnose the next constraint
For each run, identify the specific stage preventing reliable output and choose the next improvement. Continue until the workflow creates a useful result often enough to deploy.
Pro tip Require the team to articulate the next learning objective clearly.
Watch out Do not confuse repeated activity with directed progress.
- 6
Operationalize successful research
Turn validated experiments into documented, reusable systems accessible to the people who perform the work. Preserve specialist ownership of standards and constraints.
Pro tip Package the capability so users do not need to reproduce the research themselves.
Watch out Leaving successful experiments inside one expert's private workspace prevents organizational leverage.
- 7
Measure production impact
Track whether the capability shortens cycles, raises quality, expands testing, or increases team participation. Stop or redirect work that does not create measurable value.
Pro tip Evaluate outputs that actually ship rather than counting generated assets.
Watch out High AI usage can coexist with zero meaningful business impact.
- 8
Compound the capability
Update the system as models improve and as the team's production requirements become more demanding. Make technical progress improve existing workflows rather than forcing constant replacement.
Pro tip Design modular workflows so individual models can be swapped without discarding the whole system.
Watch out Chasing every new model keeps the team in perpetual restart mode.
In the wild
A marketing team repeatedly loses hours preparing YouTube thumbnail tests. Instead of asking each producer to improvise with an image model, it assigns a specialist a protected week to encode test types, analyze winning references, generate controlled variants, and measure which outputs are usable.
→ The experiment produces a proprietary testing capability that can be reused for every episode and shared across the production team.
A team finds that supplying reference images directly to a generator does not preserve its identity reliably. It treats this as a research problem, inserts a visual-design analysis stage, tests the resulting structured descriptions, and measures whether consistency improves.
→ A failed initial approach reveals a stronger architecture that becomes part of the team's reusable brand system.
Common mistakes
Counting access as transformation
Giving every employee access to GPT may create activity without production-quality results, shorter cycles, or shared organizational capability.
Demanding that every experiment succeed
R&D necessarily includes work that produces little or no immediate value. Requiring guaranteed returns discourages the deeper experiments that can create a proprietary advantage.
Using urgent delivery as research time
A team facing a next-morning deadline should often use its proven process and schedule capability work separately. Otherwise it may attempt a superficial AI shortcut that fails to meet the deadline or advance the system.
Is it for you?
Best for
It is best for leaders who need their marketing or creative organization to build durable AI advantages despite uncertain experiments and an initial efficiency dip.
Not ideal for
It is not ideal for urgent deliverables that must be completed tomorrow with no tolerance for experimentation or temporary inefficiency.
From the transcript
“The teams that are really successful are the ones that are willing to invest and and they should look at it as kind of like…”
“when they do R&D you build your own proprietary capabilities uh that nobody else has and gives you like a real advantage over others.”
“assume that some of your effort is not going to yield results because that's how R&D works.”
From the episode
Stop Prompting: Build an AI "Design App" Instead (Demo)