While artificial intelligence used to be a mystery to the common person, the barrier to entry has now been lifted for anyone to use. What used to take a typical VC-backed startup years to build can now be prototyped by a single person with the right skills in a weekend. Innovation utilizing AI was reserved for the likes of Big Tech as they were the ones capable of the infrastructure.
According to AI Product Manager Jyothi Nookula, a veteran builder with experience spanning Amazon, Meta, and Netflix, we are witnessing a fundamental shift. This democratization is not only changing how products are built; it is changing who has access to build. Nookula sees this sparking a new generation of entrepreneurship and innovation across multiple industries. As an AI product manager, she finds that anyone can learn these AI product skills and implement them into an innovative business idea or within their current role. This can lead to company wide innovation, however, there is currently a gap in AI product training amongst corporation. Closing that gap is what will make organizations stay ahead.
“Democratizing AI product skills shifts AI from being something you need a large, specialized team to access into something individuals and small teams can actually build with. Historically, meaningful AI products required deep ML expertise, infrastructure teams, and significant capital. That created a high barrier to entry and concentrated innovation inside a small number of large tech companies. What’s changed in the last few years is not just model capability, but also who can participate. What used to require a team of 10 engineers, $2M in funding, and 18 months can now be prototyped by a single person with the right skills in a weekend. But here’s the catch- knowing what to build and how to think about the problem is now the bottleneck, not technical implementation. When you teach someone AI product skills, you’re essentially handing them the keys to a factory they can operate alone.”
Understanding AI Products
An AI product does not just follow predefined rules, it can interpret information, exercise judgment, and take or recommend actions under uncertainty. Traditional software is deterministic; it executes exact instructions written in advance. AI products are probabilistic. AI products learn patterns, interpret ambiguity, and generate outputs that weren’t explicitly programmed. At this point in time, AI products act more as collaborators rather than tools, Nookula explained to me.
“Broadly, AI products can do five things that traditional software cannot: AI products can work with unstructured information- inputs that don’t fit neatly into forms or schemas like text, documents, images, conversations, audio, or logs. This allows them to build AI products that summarize meetings or long documents or extract insights from emails or support tickets or interpret resumes, contracts, or research. This shifts software from being form-driven to context-aware. AI products can classify, rank, recommend, or evaluate when there is no single “correct” answer. For example, assessing fit or quality of candidates, leads, or content. AI products can change behavior based on who the user is, what they’ve done before, and what’s happening now, leading to personalized products where the AI product feels less like tools and more like collaborators. Advanced AI products like agents don’t just suggest, they are proactive and act on behalf of their users, like drafting and sending follow-ups, creating tickets. And lastly, AI products can improve based on real-world usage, leading to self-learning systems,” she said.
Validating AI Capabilities
Nookula finds that teaching AI product skills can lead to organizations with strong product managers who understand how to scope AI capabilities, design evaluation frameworks, and think through agentic architectures while validating ideas without waiting for engineering bandwidth. “The mental models behind AI product thinking: evaluation, context architecture, and human-agent interaction, are domain-agnostic. Learn them once, and you suddenly have a lens for spotting AI opportunities in any industry you understand deeply. That’s the real unlock: domain expertise combined with a new way of seeing what’s now possible,” she added. “Many talented PMs and designers stay at big companies because they feel they need ‘the infrastructure’ to build AI products. Once they realize they can architect and ship AI-native products themselves, the calculus changes. They no longer need to “wait” for perfect technical conditions. They can prototype, test, and ship.”
Democratizing AI product skills doesn’t mean everyone suddenly needs to train models or tune parameters. It means understanding how to think with AI. How to frame problems, define what “good” looks like, design feedback loops, and decide when machines should act—and when humans should stay firmly in charge. When people learn AI product skills, they’re no longer waiting on engineering bandwidth or executive buy-in to validate an idea. They can test it themselves. They can prove value before writing a pitch deck. And once experimentation becomes cheap, entrepreneurship becomes inevitable.
An AI Product Manager within an organization is a product leader responsible for designing, shipping, and evolving products where AI is core to the value delivered, not just a feature added on. In traditional product development, product managers define requirements and engineers build deterministic systems, if X happens, do Y. With AI-native products, AI Product Managers are working with probabilistic systems. The AI feature might work differently each time it runs. This means the AI Product Manager is designing the entire QA process, edge case handling, and reliability guarantees; all of that has to be completely rethought. Nookula explained that they’re not just testing if something works; they’re testing if it works well enough, consistently enough across a distribution of outcomes.
Nookula shared what an AI Product Manager’s daily tasks can look like:
Evaluation: Reviewing model outputs, refining what “good” looks like, building rubrics, and analyzing failure cases. It’s a continuous practice that shapes the product.
Context and data decisions: What information does the AI need to do its job? Where does it come from? How fresh does it need to be? This is architectural work that directly determines product quality.
Boundary design: Deciding where the AI acts autonomously versus where humans stay in the loop. These decisions are nuanced, high-stakes, and constantly revisited.
Experimentation: Running structured tests to understand how changes affect output quality. Traditional A/B testing, but also qualitative evaluation of generations and behaviors.
Cross-functional translation: Helping engineers, designers, and leadership understand what’s possible, what’s hard, and what’s risky.
Prompt and context iteration: Hands-on work shaping how the system behaves. In many AI products, this is the product work.
On that same note, domain experts are now leveraging AI to stay ahead of the curve in their industry. The most interesting founders in this new wave aren’t always career technologists. They’re healthcare operators, legal experts, finance professionals—people who’ve lived with broken systems for years and finally have the tools to fix them. AI product thinking is domain-agnostic. Once you learn the mental models, evaluation, context architecture, human–agent interaction, and suddenly you can see opportunities everywhere. Problems that felt too complex, too manual, or too “that’s just how it is” start to look solvable.
Utilizing The Next Wave
Nookula forecasts the next wave of AI products will be always-on systems, quietly managing workflows. “The first wave of AI products were discrete– a chatbot, a summary button, a recommendation panel. The next wave acts on their behalf. We’re moving from ‘AI that helps you write an email’ to ‘AI that manages your inbox.’ This makes AI less visible but far more impactful. This will change product architecture, trust models, how businesses think about headcount and workflows. Agents will orchestrate work across systems, moving beyond AI that responds to prompts toward agentic systems that coordinate actions. In practice, this means AI products that break down goals into steps, interact with multiple tools and data sources, ask for clarification when needed, and escalate to humans when confidence is low. This changes how work gets done—not by replacing humans, but by handling coordination overhead.,” she added. “Additionally, context becomes the Moat as the models themselves are commoditizing. What differentiates AI products now is context, how well they understand your business, your workflows, your history. The winners will be products that build rich, persistent context layers: memory systems, knowledge graphs, and integrations that accumulate understanding over time.”
So, where does this leave human involvement? Evaluatory roles will become a core business capability to evaluate quality, reliability, consistency, and alignment, not just accuracy. Nookula also shared that vertical AI specific to industries and workflows will outpace general AI tools, such as an AI that understands pharmaceutical regulatory filings. Humans will also be needed, like AI Product Managers, to ensure compound systems are fed the correct context and information to architect the right models and route tasks intelligently.
From solo founders, small startups, to big teams, utilizing and understanding how to leverage AI products can allow for company wide innovation that historically would take years.
