How to Move from AI Pilots to Real Products, with Anna Litvak-Hinenzon

AI is everywhere right now, but shipping AI that people actually use is still hard. Many teams can get a model working in a demo, then watch the project stall when it hits real users, real data, and real expectations.

The difference usually isn’t the algorithm. It’s whether the work is tied to a real business goal, backed by reliable data and operations, and designed in a way that earns trust.

In this episode of Boston Leaders Voices, we sat down with Anna Litvak-Hinenzon to discuss why so many AI initiatives miss ROI, what breaks between prototype and production, the capabilities teams need to close that gap, and how leaders should think about responsible use as AI becomes part of everyday work and life.

Meet Anna Litvak-Hinenzon: Turning AI Into Real Business Outcomes

Anna Litvak-Hinenzon is an AI product and technology executive who has spent her career helping organizations turn data and AI into real business outcomes. She has worked across industries including healthcare and pharma, consumer goods, and market research, leading teams, building new functions from scratch, and supporting major digital transformation efforts.

She holds a master’s in computer vision and a PhD in applied mathematics, and has been building AI-powered products long before the current wave made AI a mainstream conversation. Today, at Al-Tech.Ai, she helps enterprises shape AI strategy and build AI-centric products designed for customer value and long-term adoption.

Anna also teaches in Georgetown University’s AI management master’s program, working with professionals who want to lead AI teams and initiatives inside their organizations.

Building AI People Can Trust

Our conversation with Anna covered what it takes for AI to move beyond experimentation and become something teams can maintain and improve over time. We talked about trust and adoption, the work behind productizing AI, and the capabilities teams need to make these initiatives succeed.

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🎧 If you’d prefer to hear the full discussion, the podcast episode is available below. Otherwise, keep reading for a recap of the highlights.

Why Many AI Initiatives Never Reach ROI

A lot of teams don’t get stuck because they “can’t build AI.” They get stuck because the initiative starts without a clear answer to a simple question: do we have a plan and strategy that makes sure we reach ROI?

Anna points out that as much as 95% of AI initiatives fail to deliver ROI, often when strong technical work isn’t tied to a measurable goal and a problem users actually feel.

“That’s one of the reasons why at Al-Tech.AI, we developed a five-stage plan to make sure that your company is one of the 5% that are successfully implementing it.”

The Real Hurdles Behind Productizing AI

Anna breaks productizing AI into three parts: technology, data, and people. While the technology is often the easy part, things get harder when teams have to make data reliable in production and get real users to adopt the product.

“People are much more lenient to human errors than to AI or technology errors.”

That’s why she pushes for heavier investment in testing and accuracy. At the same time, she notes that adoption depends on an experience that feels intuitive and “delightful” to use.

Choosing AI For The Right Reasons

For Anna, one of the biggest reasons AI initiatives fall short is that companies start with the tool instead of the goal. When “adding AI” becomes the objective, teams can end up building something that sounds impressive but doesn’t solve a real problem, or doesn’t justify the effort once it’s time to measure impact.

Instead, she argues the work has to run in the opposite direction: start from the company’s goals, identify where AI can uniquely help, and only then decide what to build (and how you’ll measure success).

“You can never use technology randomly for the sake of some emerging new gadget.”

Using AI Without Losing The Skills Behind It

Anna shifts the conversation from “what AI can do” to what constant AI use can do to us. If we outsource too much (writing, problem-solving, even basic decisions), we may save time in the moment, but slowly stop practicing the skills we still need, especially creativity and critical thinking.

“We need to make sure that when we use AI, we use it as an assistant and not as a replacement.”

That’s why she stresses keeping a “human in the loop”, and sometimes that human is simply us.

The Skills Teams Need To Productize AI

Anna’s point here is that moving from a pilot to a real AI product takes a different skill set than building the first model. Teams need people who can handle the data side (cleaning, pipelines, infrastructure), keep models performing over time (MLOps/LLMOps), and, when relevant, get good at prompting.

“You need to make sure you have what today are generally called AI engineers.”

She describes “AI engineers” as an emerging role that blends what used to sit across data engineering, data science, and machine learning, plus the hands-on work required to build and maintain AI-powered systems. She also adds that leadership needs AI literacy to make good calls on what’s possible, what’s risky, and what it will cost.