AI agent demos go viral. AI agent revenue? Not so much. Here’s why 80% of enterprise AI projects never make it to production—and the 4-step framework that changes that. Samuel W. Ho has watched this play out more times than he can count. As the former head of product at Turing, a series E unicorn working on AGI and large language models, Sam brings experience from Google, Intel, Glassdoor, Kenshoo, and Sendoso. His track record shows something most AI teams miss: the fancy technology doesn’t matter if you can’t connect it to real business results.
Start With A Business-Backed Hypothesis
Here’s where most teams get it wrong. They fall in love with what the technology can do and then scramble to find a use case. Sam flips this completely. “Before touching a model, identify one workflow that clearly ties to revenue or efficiency and ask yourself, which metric matters most this quarter?” he explains. It sounds obvious, but you’d be surprised how many companies skip this step.
The goal needs to fit in one sentence. No hand waving, no “we think it might help with engagement.” Sam gives a concrete example: “If an agent drafts tier one support replies using our help center, we cut handle time by 25%.” See the difference? There’s a specific task, a clear method, and a number you can actually measure. “Start small, win one lane, and expand only after you see results,” he says. Trying to solve everything at once is how you end up solving nothing.
Building Smart, Not Complex
Complexity kills more AI projects than bad technology ever could. Sam learned this lesson the hard way over the years. “Great prototypes are simple. Use a curated data set, structured prompts, and just one or two essential tools,” he notes. You’re not trying to build the finished product yet. You’re testing whether your core idea actually works. Safety needs to be baked in from the start, not bolted on later. “Add safety immediately with retrieval grounding, abstain on low confidence, and always include a human in the loop,” he stresses. These aren’t nice-to-haves. They’re the difference between a controlled test and a PR disaster. At Turing, Sam saw this principle pay off repeatedly. “Our fastest wins came from agents that did one thing extremely well before scaling.” Narrow beats broad when you’re proving out a new approach.
The Science of Measurement
Most companies treat AI measurement as casually as checking the weather. They look at a few numbers, decide things seem better, and move on. Sam takes a harder line on this. “Track what matters, whether that’s coverage, quality, time saved, and business lift. Make sure you compare against a control group so you know what’s real,” he explains. Without controls, you’re just fooling yourself about what’s actually working. His time at Kenshoo taught him to be ruthless about proving causation. You can’t just assume the AI agent caused the improvement you’re seeing. Maybe your support team got better training that same month. Maybe the product became easier to use. “Causality beats correlation. True impact builds trust,” Sam states. That trust becomes essential when you need more budget or when someone questions whether any of this AI spending makes sense.
Scaling With Discipline
Success brings its own trap. The agent works great in one department, and suddenly everyone wants it. Executives start asking why you’re not deploying it company-wide. Sam pushes back hard on this impulse. “When you see lift, don’t flip the switch everywhere. You want to convert wins into playbooks, automate only where performance is proven, and keep a tight quality loop,” he cautions. Rushing to scale can blow up something that was working perfectly fine at small scale. Each win should make the next deployment easier, not just faster. Document what worked and why. Figure out what made this particular use case successful. “Celebrate early ROI; momentum fuels adoption,” he adds. Teams need proof that this stuff actually works before they’ll trust AI with anything important.
Sam boils down his entire approach to four steps: “Scope it, ground it, measure it, and scale it. That’s how you build AI agents that move the metric, not just the demo.” Demos get you meetings and maybe some press coverage. Metrics keep your company in business. The framework isn’t rocket science, but most companies still get it wrong. They skip the business case because they’re excited about the tech. They build something complex because simple feels too easy. They eyeball the results instead of measuring properly. They scale before they’re ready because pressure builds. Ho’s approach works because it forces discipline at every step. Start with a clear business problem. Build the simplest thing that could work. Prove it actually does work. Then, and only then, roll it out more broadly.
Connect with Samuel W. Ho on LinkedIn to explore how disciplined AI strategies drive measurable business growth.
