Spark
Reimagining creative intake as an intelligent campaign-building experience.
I led the product strategy and validation of an AI-enabled workflow that transformed fragmented creative requests into complete, structured campaigns—reducing brief creation time by 90% while improving the quality of information teams received.
Turning an intake problem into a product opportunity
Creative teams were losing time before work even began. Requests arrived incomplete, campaign context was distributed across multiple briefs, and customers repeatedly entered the same information for every deliverable.
The challenge
Simplify a highly variable creative intake process without removing the detail production teams needed to execute confidently.
My approach
Reframe intake around campaign intent, use AI to structure the request, and validate the highest-risk assumptions before committing to implementation.
The result
A validated product direction that reduced brief creation time by 90% and created a more scalable foundation for campaign intake.
The workflow optimized for collecting fields—not creating momentum
A customer could spend hours translating one campaign into separate requests. Internally, incomplete context produced clarification cycles, delayed starts, and inconsistent execution across related deliverables.
AI transformed repeated request cycles into one structured campaign
Previously, customers either submitted one request for every deliverable or created an oversized multi-deliverable brief that required the internal team to separate and interpret it. Spark preserved the necessary production detail while removing repetitive setup and identifying missing information before submission.
Build and review every request separately
Every deliverable required its own production-ready combination of campaign context, brand direction, specifications, source assets, copy, and requirements.
Re-enter shared campaign details for each channel, format, size, and variation.
Bundle multiple deliverables into one brief that must be interpreted and separated internally.
Production begins only after requests are interpreted, divided, clarified, and confirmed complete.
The consequence Repeated customer effort, manual interpretation, delayed starts, and campaign context fragmented across multiple briefs.
Describe the campaign once
Spark converts campaign intent into a connected set of structured, production-ready deliverables.
The improvement One connected campaign, one customer review point, earlier risk detection, and consistent context across every deliverable.
I grounded the strategy in customer behavior and operational reality
I combined customer interviews, workflow observation, request data, and cross-functional input to understand where customers struggled, where internal teams absorbed that complexity, and which problems represented the greatest product opportunity.
Customers thought in campaigns
The existing experience forced customers to translate one campaign into a series of disconnected deliverable requests.
Completeness was subjective
A request could pass form validation while still lacking the context production teams needed to begin work.
Complexity moved downstream
Simplifying intake without structuring the output would only shift additional interpretation and coordination onto internal teams.
The form was not the fundamental problem
Customers needed help translating intent into production-ready requirements. That reframed the opportunity from form optimization to intelligent campaign orchestration.
I aligned the organization around the problem before defining the solution
The opportunity crossed customer experience, production operations, platform capabilities, and AI risk. I established a shared product thesis, clarified decision criteria, and created alignment around what the first release needed to prove.
Set the product direction
Reframed the roadmap from incremental form improvements to a campaign-level workflow centered on customer intent.
Managed product risk
Prioritized assumptions around AI usefulness, customer trust, output completeness, and operational handoff.
Built alignment
Connected executive priorities, customer needs, technical constraints, and operational requirements into one strategy.
The first release was designed to answer the riskiest questions
Instead of attempting to automate the entire production process, I scoped the MVP around the moment where AI could create immediate customer value: converting campaign intent into structured, reviewable deliverables.
What the MVP needed to prove
- Customers could describe campaign intent conversationally
- AI could identify missing production requirements
- One campaign could generate multiple connected deliverables
- Customers could review and refine outputs before submission
- Structured results could support the existing production workflow
What we intentionally did not solve yet
- Autonomous generation of finished creative assets
- Replacement of production workflow systems
- Advanced workflow configuration for every edge case
- Full automation of approval and governance processes
- Broad expansion before validating the core interaction
I translated strategy into decisions each team could execute
I led the work across discovery, scope, interaction design, feasibility, validation, and launch planning while keeping teams aligned on the customer outcome and evidence required for each decision.
Strategy and prioritization
Defined the product thesis, success criteria, MVP boundaries, decision framework, and validation plan.
Interaction and trust
Partnered on progressive disclosure, campaign review, AI transparency, and error-recovery experiences.
Feasibility and confidence
Aligned platform capabilities, structured output requirements, AI confidence thresholds, and technical constraints.
Handoff and adoption
Ensured outputs supported production needs and shaped launch readiness, customer education, and feedback loops.
A guided experience that turns intent into structured work
Spark progressively builds the campaign with the customer. Each stage reduces ambiguity while preserving the brand, format, asset, content, and production details required for a confident handoff.
Conversation
Customers begin with the language they already use to describe their work: the campaign goal, audience, message, timing, and creative direction.
- Starts with intent instead of form fields
- Uses progressive prompts to reduce cognitive load
- Preserves customer control throughout the experience
What are you launching, and what should this campaign help your audience do?
We’re launching a new business plan for growing teams and need a coordinated campaign across email, web, and social.
I can help structure that. Who is the primary audience and when does the campaign launch?
Generation
Spark translates shared campaign context into a connected set of deliverables, reducing repeated setup while keeping each output independently actionable.
- Generates a coordinated deliverable set
- Reuses shared campaign information consistently
- Separates deliverable-specific requirements automatically
Application
Customers review how Spark applied formats, dimensions, source assets, brand context, copy, and requirements across the campaign before anything is submitted.
- Makes generated decisions visible and editable
- Connects shared inputs to individual deliverables
- Surfaces completeness before production handoff
Validation demonstrated speed, comprehension, and customer trust
I designed the validation plan around behavioral evidence—not simply whether customers liked the concept. We measured whether participants could create campaigns faster, understand AI-generated outputs, and identify what they wanted to change.
Faster brief creation during validation compared with the existing request workflow.
Customers understood the campaign model
Participants naturally described their work at the campaign level and recognized how deliverables connected.
Visibility supported trust
Customers wanted to see what Spark applied and retain the ability to review or modify generated details.
Prompts reduced missing context
Completeness guidance surfaced risks while the customer could still resolve them without a separate clarification cycle.
The concept established a scalable direction for intelligent intake
Spark validated that AI could simplify a complex B2B workflow without sacrificing customer control or production quality. It also created a strategic foundation for structured campaign data, orchestration, and future workflow intelligence.
Faster completion
Reduced the time required to translate a campaign into actionable creative requests.
Connected campaign
Replaced repeated deliverable setup with one shared source of campaign context.
Risk detection
Used completeness confidence to surface missing information before production handoff.
Spark shifted the product conversation from form efficiency to intelligent workflow orchestration.
The work demonstrated how customer intent, structured data, and AI assistance could combine to create a faster experience today while enabling a broader platform strategy over time.
I defined success beyond initial workflow speed
The post-launch measurement plan connected customer adoption, request quality, operational efficiency, and business value so the team could improve the experience without optimizing one part of the system at the expense of another.
Campaign completion
Percentage of started campaign sessions successfully submitted.
Time to submit
Median time from starting the experience to campaign submission.
First-pass completeness
Percentage of submitted campaigns that can begin without clarification.
Revision frequency
Number and type of changes customers make to AI-generated outputs.
AI acceptance
Percentage of generated deliverables accepted with minimal editing.
Operational efficiency
Reduction in clarification time and manual campaign decomposition.
The most valuable AI products do more than accelerate a task—they redesign the workflow around the outcome customers are trying to achieve.
Spark reflects how I lead complex product opportunities: understand the system, align teams around the customer problem, reduce the highest risks first, and connect product decisions to measurable business value.
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