9 Criteria for Successful AI Projects

The AI landscape is shifting fast. The rush to launch “something with AI” is giving way to a tougher reality: proving your system works, scales, and pays for itself. Teams that succeed treat AI like any other high-stakes business investment, with focus, iteration, and a plan for what happens when things go wrong.

At HumanSignal, we’ve worked with teams from their very first experiment to global deployments across hundreds of workflows. We’ve seen the patterns that separate AI projects that deliver real impact from those that fade out.

Below, I’ll walk through a checklist we use to help teams stay on the right side of that line.

1. Own a Business & Data Moat — and a Verifiable Business Thesis

The short-lived era when you could raise endless VC money to sprinkle “AI” on a problem you’d never thought about before is gone. Everyone has access to powerful models and dollar signs in their eyes. What they don’t have is your domain expertise, your customers, and your data.

• If you’re a world-leading financial institution fighting fraud around the world, you have a unique insight to all the corners of scum and villany trying to use your network – that’ll make a powerful model.
• If you’re a popular video game publisher, speeding up approvals means more games and less bugs – turning your historical data into revenue.
• If toxic speech threatens to make your social media site worthless, you’ll need to retain your models as quickly as new slang ricochets around the web.

But in all cases, you need to understand the business impact and why your team has unique resources to solve it.  Be excited about the technology, but start with the business problem. Have a null hypothesis: a way to prove the AI isn’t beating your legacy system, so you’ll know when it is.

2. Track Your Success

This is project management 101: you need to know how close you are to the outcome you want. That could be tracking the ratio of thumbs-up/thumbs-down ratings, running the old process in parallel for a subset, or benchmarking against competitors. A quick test for “beta-ready”: the cost of wrong answers (false positives and negatives) is lower than the cost of the old way. An even quicker test for the health of a project is that they can calculate that ratio.

For tips on building benchmarks that actually matter, see Why Benchmarks Matter for Evaluating LLMs.

3. Generate Realistic Synthetic Data (Free of PII)

Your real data is your moat, but user data is sacrosanct, and your compliance team will rightly fence it in ways that hurt development speed. Without sample data, or with a small hand-generated corpus, you’ll move slowly, miss edge cases, and stall the moment you need to test a new feature. Generating synthetic data from prompts can be a surprisingly good way to start (here’s how).

This is also our most selfish ask: Not only does it help your team to know they can check data into source control without fear but it makes life so much easier when you can give our success team representative data to test with or even share examples we can bake into our internal test suites.

Sample data isn’t just a convenience. It’s often the difference between staying stuck at zero and getting to beta.

4. Start Small, Iterate Fast

Call this project management 102. Aim at a narrow, high-value, measurable use case before you try to “AI all the things.” There are dozens of “everything a doctor needs” AI companies, what makes BioticsAI successful is its dedication to detecting “errors in fetal ultrasound screenings”. Smaller scope means faster iteration, but in AI it’s necessary to make the problem tractable at all.

Once you have something in users’ hands, the real data starts pouring in. So:

5. Plan for More Testing & Continuous Evaluation

Non-deterministic systems mean more testing, not less. Every new surface area adds complexity. Data can get more antagonistic, too. Users who could never trigger a buffer overflow against your API could be very persuasive, asking for free flights when you give them a natural language interface.

Automation & ongoing evaluations is even more important for AI systems, beyond the usual benefits for development speed it’ll keep you protected when vendors update models or data drifts.

The upside: debugging is more fun when you do find a bug. It feels like working a puzzle alongside the model, not just checking boxes (case in point). The most valuable insight into model improvements can be found where models fail, part of the reason your data is your moat is that you have found those edge cases.

Protip: Custom benchmarks are a great way to track your quality at every step on your journey.

6. Use Modular Workflows

I like to think of agentic workflows as the logical continuation of good systems design. Remember the early days when LLMs could sort of do math? Imagine retraining the whole model on multiplication tables until they clogged up the corpus enough to get them correct most of the time instead of just handing it a calculator. Separating specialist AIs (or microservices) handling the most difficult, error-prone, or high-impact parts of your process makes debugging tractable.

The warning: if there isn’t at least one agent that absolutely needs your moat, you don’t have a defensible system.

7. Be Ready to Swap Tools & Models

Models evolve, change, and improve faster than ever. Hundreds of companies cry out in agony at every Open AI demo.  Structure your system so you can benchmark and replace them without dismantling the whole application.

This is another way where agentic workflows shine, and the good news is so much work is being done in the open, so whether you’re using OpenAI’s JSON API or Anthropic’s Model Context Protocol that interoperability is quickly implemented across many players in the ecosystem.

Evergreen advice: Don’t fall prey to “Not Invented Here Syndrome”, use supported standards whenever possible.

8. Watch Your Infra Spend

AI can burn through your budget as fast as you’ll let it. That’s fine if value scales with spend, but lethal if it doesn’t.

With a modular workflow, you can dial model quality up for high-impact tasks and down for others. Benchmarks will tell you when you can safely demote an agent from a cutting-edge LLM to something cheaper (and faster — which users love).

What we’ve seen: Many of the best teams use the largest models during development, and then use those models to train (or at least benchmark) against smaller, faster models. This balances cost savings against using dev time most efficiently and also sets you up to be able to test & swap new models as they come out. Sometimes you can truly get a cheaper, faster, and better outcome by changing a version number in an API call – a rare win in software development.

9. Keep People in the Flow

Even the best AI will fail sometimes. Decide what happens when it does and when you escalate to your experts. Our in-house AI assistant, for example, suggests filing a ticket if it can’t answer in three tries. When you escalate to a human, make sure that the case goes into retraining. Yesterday’s failure is today’s training case and part of tomorrow’s benchmark.

Remember: Quality is what people say it is.

The Bottom Line

Most AI projects don’t fail because of bad models; they fail because they are heavy on novelty and  succeed. You need a viable thesis, a way to measure progress, a way to make progress, and of course: humans in the loop.