David Hunt / Independent product builder
Useful AI needs more than a promising demo.
I created Skip The Hype to close the gap between experimenting with AI and putting it to work. I help businesses choose one valuable problem, build the smallest useful version, and learn from how it performs in the real workflow.
01 / Why Skip The Hype exists
The interesting part starts after the experiment.
A useful prompt or impressive demo can prove that something is possible. It does not automatically make the work dependable, understandable, or ready for a team to use.
The work I care about is the part that comes next: choosing the right problem, fitting the technology into the way people actually work, and building enough to find out whether it deserves to go further.
AI becomes useful when it improves a recognizable part of the business and the people responsible for that work understand what it does, where it stops, and what still needs their judgment.
- 01
Choose the valuable problem.
A new capability is interesting. A capability attached to an important workflow is something you can evaluate.
- 02
Build the smallest useful version.
Scope the first implementation tightly enough to finish, use, and learn from without pretending the first version has proved everything.
- 03
Keep control visible.
People should know when AI prepared a draft, what needs review, and who remains responsible for the final decision.
02 / Experience and evidence
Product judgment built through the work.
I have spent more than 10 years working with applied AI. Along the way, I have built digital and AI products, led product teams, and worked across product design, software, strategy, and delivery.
Before starting Skip The Hype, I was an employee of Publicis Groupe. I worked there as a consultant and product partner with Pfizer, Coca-Cola, British Airways, and Mars, and became VP and Head of Product for MRCL, Publicis Groupe’s custom AI platform.
Some of that work concerned broader digital experiences rather than AI. Those companies are not Skip The Hype customers. The relevant thread is the judgment the work required: choose the problem carefully, make the technology understandable, and build something people can actually use.
- Applied AI
- More than 10 years
- Previous role
- VP and Head of Product for MRCL
- Disciplines
- Product, design, software, strategy, and team leadership
- Availability
- Los Angeles and remote engagements
03 / How I work
One builder, from the first question to the handoff.
You work directly with me throughout. The person helping choose the opportunity is also responsible for building the first version, explaining the trade-offs, and leaving the work in a form your team can understand.
- 01 / FIND
Find the problem worth solving.
Start with the workflow, what has already been tried, and the outcome that would make the work meaningfully better.
- 02 / DEFINE
Agree on a useful finish line.
Choose one deliverable and make the assumptions, review points, and limits explicit before implementation begins.
- 03 / BUILD
Put a first version into the real workflow.
I build the agreed deliverable and test it with the people who understand the work, using existing tools where they fit and something new when it is justified.
- 04 / LEARN
Hand it over and decide what deserves more.
The result includes practical documentation and a clear recommendation to improve, expand, or stop.
04 / Why focused implementation
Make one thing useful before making the program bigger.
Broad transformation programs can create a great deal of activity before anyone learns whether a particular workflow is worth changing. I prefer a contained implementation with a clear question and a result people can use.
A focused 14-day Quickstart produces one agreed first version. Larger opportunities can become further sprints or a phased engagement. The point is not to make every ambition fit into two weeks. It is to create evidence before committing to more.
05 / Scope, control, and trust
Useful does not mean automatic.
A responsible implementation makes its boundaries easy to see. Before building, we identify the data involved, the decisions that require human review, and the questions that belong with an appropriate specialist.
- Human control
- Drafts, recommendations, approvals, and completed actions stay distinct. Consequential decisions remain with the people responsible for them.
- Data and policy
- The implementation must work within your policies and the guidance that applies to your organization.
- Specialist advice
- I don’t provide legal, privacy, security, or compliance advice. I will identify questions that need review rather than quietly treating them as solved.
06 / What happens next
Bring the problem. We’ll decide whether there is something useful to build.
A free 15-minute call is enough to start. Tell me where an experiment has stalled or which part of the business you want to move forward. I’m based in Los Angeles, work remotely anywhere, and can arrange local collaboration or travel separately when useful.