I’ve been talking with product managers building AI products, and a pattern emerges: the traditional product management playbook doesn’t quite apply.

AI products have characteristics that break conventional frameworks. PMs who succeed are developing new approaches. Those who apply old frameworks rigidly struggle.

Here’s what’s changing.

The Uncertainty Problem

Traditional product management assumes reasonable predictability. You research user needs, design solutions, estimate effort, build, and ship. Outcomes can be forecast from inputs.

AI products are less predictable:

Model behavior isn’t fully determinable. You can influence it with training data and prompting, but you can’t specify exact outputs for all inputs. The product’s behavior emerges from training, not from explicit programming.

Capability boundaries are fuzzy. For a traditional product, you know what it can and can’t do. For AI products, you know generally but not precisely. Edge cases surprise you.

Quality is probabilistic. “How accurate is it?” doesn’t have a single answer. It depends on the specific input, and you measure distributions rather than guarantees.

This uncertainty requires different planning approaches. Roadmaps are less reliable. Scope estimates are less precise. Launch criteria are more nuanced.

Evaluation Becomes Central

For traditional products, evaluation happens at the end - user testing, analytics, feedback. For AI products, evaluation needs to be central and continuous.

Pre-launch evaluation. Before shipping, you need comprehensive testing against real-world scenarios. Not just “does it work” but “how often does it work, for what types of inputs, and how does it fail?”

Continuous monitoring. AI performance drifts. User patterns change. What worked at launch may not work months later. Monitoring isn’t optional.

Evaluation infrastructure. Building the systems to measure AI performance is a major investment. Many teams underinvest here and suffer later.

PMs who succeed with AI products prioritize evaluation capability alongside feature development.

User Expectations Are Different

Users don’t know how to interact with AI products initially. And their expectations are calibrated by consumer AI experiences that may not match your product’s capabilities.

Expectation management is crucial. Users expect ChatGPT-level capability from every AI feature. If your narrow, specialized AI doesn’t match that, you need to set expectations clearly.

Failure handling matters more. Users are somewhat forgiving of AI errors if the product handles them well. Unhandled failures, silent errors, or confident wrong answers destroy trust.

Learning curves exist. Effective use of AI products often requires learning how to prompt, what to expect, and how to interpret outputs. This learning curve is part of the product experience.

Trust is fragile. A few bad experiences can make users distrust the AI entirely. Building and maintaining trust requires deliberate attention.

Feature Definition Changes

Traditional feature specs are deterministic: input X produces output Y. AI feature specs are probabilistic: input X usually produces something like Y, with these variations and failure modes.

Success metrics are different. “90% accuracy” might be great for some use cases and unacceptable for others. Defining what “working” means requires more context.

Edge case handling. You can’t enumerate all edge cases. You need fallback behaviors, uncertainty expressions, and escalation paths.

User control requirements. Users often need ways to correct AI outputs, provide feedback, or opt out. These controls are part of the core feature, not afterthoughts.

Iteration is essential. First versions are rarely good enough. Planning for rapid iteration on prompts, training data, and model selection is necessary.

Team Dynamics Shift

Building AI products requires different team compositions and collaborations:

ML expertise on product teams. Product decisions require understanding AI capabilities and limitations. PMs need access to people who can answer technical feasibility questions.

Data science involvement. Understanding model performance, building evaluation systems, and analyzing results requires data skills that product teams may not traditionally have.

Tighter iteration loops. The gap between “build” and “evaluate” compresses. Teams need to test quickly and often.

Cross-functional alignment. AI products touch more functions - ML, data, engineering, design, legal, compliance. Coordination is more complex.

New PM Skills

PMs building AI products need additional capabilities:

AI literacy. Not building models, but understanding what’s possible, what’s hard, and what questions to ask. This is a learnable skill.

Evaluation design. Defining what good looks like, building test sets, interpreting results. This is increasingly core PM work.

Uncertainty communication. Explaining probabilistic outcomes to stakeholders, users, and executives. “It works 90% of the time” requires context.

Ethical reasoning. AI products have unique ethical considerations. PMs need frameworks for navigating these.

Technical partnership. Working closely with ML engineers on capabilities that can’t be specified like traditional features.

What I’m Recommending

For PMs transitioning to AI products:

Invest in evaluation infrastructure early. This pays dividends throughout the product lifecycle.

Plan for iteration. Your first version won’t be your last. Build for changeability.

Manage expectations explicitly. User onboarding should set appropriate expectations about capabilities and limitations.

Build feedback loops. Systems that capture whether AI outputs were helpful and use that to improve are essential.

Partner with technical leadership. AI product decisions often involve technical judgment. Close partnerships with ML leads are valuable.

Learn the fundamentals. You don’t need to build models, but understanding how they work makes you more effective.

Organizations building AI products often benefit from working with AI consultants Melbourne who have experience navigating these product management challenges.

The Opportunity

Product management for AI is harder in some ways - more uncertainty, more evaluation, more complexity. But it’s also an opportunity.

The PM skills that work for AI products are still developing. There’s room to define best practices. PMs who develop these skills early will have advantages.

And AI products can be genuinely transformative when they work. The challenge of managing uncertainty is worth it for the potential impact.

The frameworks are still forming. That’s uncomfortable and exciting. Team400 and others building AI products are figuring this out in real time, and the learning is accelerating.

For PMs, the message is clear: lean into the differences, develop new skills, and embrace the uncertainty. The playbook is being written now by those willing to navigate uncharted territory.