Fast AI Prototypes Need Two Questions: Strategy and Optimization

Posted on Oct 15 • Originally published at Medium • Fully Autonomous

AI changes how quickly a small team can put an idea in front of someone. A pair of builders can sometimes produce a bounded prototype in days that would once have waited through weeks of departmental handoffs. That is an opportunity to shorten the distance to useful feedback. It is not a universal productivity benchmark or a promise about production readiness.

Disclosure: An AI agent drafted this article from my direction, personal background, public project documentation, and the linked technical sources.

In my consulting conversations with small-business owners and executives at large technology companies, I keep returning to two questions. Strategy: are we building the right thing? Optimization: can we make it work better with the time, money, and infrastructure available? A team can write code very quickly while leaving both questions unanswered.

My public projects include supplier-price workflows, a voice operations product, a community guide, application tools, church operations, healthcare software, and forecasting research. They are not interchangeable success stories. Some are delivered work; others are ongoing products, staged migrations, or research prototypes.

Across professional projects and conversations spanning technology, real estate, insurance, healthcare, education, finance, and crypto, the domain changes what counts as a useful result. My experience across those settings is why I start with the workflow and the person using it. A faster interface does little if it accelerates the wrong decision.

For a first version, I would write down a person, a recurring task, and an observable improvement. “Help staff review an incoming record” is something you can test. “Transform the entire business with AI” leaves the team guessing what to build and the reviewer guessing what success means.

Then choose an experiment that could change your mind. If the real difficulty is inconsistent source information, an attractive dashboard will not settle it. If the difficulty is that nobody knows who should act next, another generated summary may add to the queue.

This is where making mistakes early is productive. An inexpensive prototype can reveal that a proposed workflow is confusing or that a feature is unnecessary. Retain the observation, revise the assumption, and try again before expanding the implementation.

Once a useful path exists, optimization becomes concrete. You can inspect repeated work, slow integrations, unnecessary model calls, oversized context, and the steps that still require someone to intervene. Pick the constraint that affects the outcome rather than optimizing whichever metric is easiest to display.

In TMC Price Intelligence, the documented work connects supplier emails and attachments to structured pricing and subsequent quote and invoice workflows. In SmartVoiceX, calling connects to scheduling, follow-up, and human handoff. The relevant unit of improvement is the useful workflow, not just an isolated model response.

For a new pricing tool, I would first check whether two offers are comparable. Only then would I spend time reducing the model work needed to process the next batch. Making an unreliable comparison cheaper does not make it useful.

Anthropic's December 19, 2024 guidance on effective agents recommends beginning with simple solutions and increasing complexity when warranted. It also recognizes the cost and latency tradeoffs of agentic systems. That older architectural advice remains relevant to a current temptation: adding another agent or reasoning step before identifying the problem it solves.

A rigid demand for a perfect first attempt can encourage long instructions built around imagined failures. Unbounded retries create a different expense. I prefer a bounded experiment with a clear question, a reviewable result, and a decision about the next attempt.

When an experiment fails, record what it taught you. A confusing screen might lead to a changed interaction. A recurring extraction error might become an example or a test. An unnecessary generation step might disappear entirely. Fewer repeated investigations and smaller, relevant inputs can reduce token use, but the saving needs to be measured across the completed workflow.

That kind of improvement combines technical and creative work. Technical judgment helps you understand the constraints and verify behavior. Creative judgment helps you question whether the current design is the best way to meet the need. Speed becomes useful when it gives both kinds of judgment more opportunities to act.

My aim is to help people build their product and understand how to own it. Continued support can be a deliberate choice after handoff. It should not be necessary because the owner cannot find the source, understand the configuration, or make a routine change.

For a handoff, I would expect the owner to be able to locate the code and deployment process, understand recurring costs, change a key configuration, inspect a failed run, and recover a previous version. Growth then has a practical meaning: someone knows which parts must change when usage, data, or team size increases. A generated codebase alone does not establish that an implementation will scale.

Teaching is part of how I approach that problem. My seminars and events create space to discuss practical workflows. At AFROTECH, Building AfroTech.guide will use a specific build to explain the process and give attendees steps for starting their own AI-powered project.

Small teams now have more opportunities to test an idea before committing to a large build. Use that opportunity to resolve the strategy question, improve the working path, and transfer the knowledge. The owner should finish with a product they can operate and a better understanding of how to improve it.