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How Do I Know If the Workflow Is Real Before Building AI?

In the rush to build AI-powered products, there’s a common pitfall: focusing too much on the model and too little on the actual workflow that users need. I’ve seen this frequently across early-stage B2B SaaS https://highstylife.com/how-do-i-write-ai-helped-create-this-labels-that-users-trust/ initiatives and even mature organizations like PayPal or AI innovators such as H2O.ai. The model is never the product — the workflow is. Before you write a single line of AI code, you must ask:

  • What is the user trigger? When and why does the user come to this problem?
  • What is the desired outcome? How do we know the workflow worked for the user?
  • Is the problem truly clear and validated? Do we understand the user’s context and constraints?

This blog post walks through how to define and validate a real workflow before you build AI. It’s grounded in practical frameworks like the PM Toolkit calculators, tried-and-true spreadsheet-driven evaluation approaches, and patterns I’ve collected shipping AI workflows in the wild. If you want to avoid building a fancy “model” that no one actually uses, read on.

1. The Model Is Not the Product: Focus on Workflow Definition

One of my ship/no-ship thresholds is that teams must start with a clear workflow definition before any modeling work begins. This means articulating:

  1. User trigger: What event, pain point, or routine action prompts the user to turn to this workflow? These are the “when and why” of usage.
    2. https://smoothdecorator.com/what-does-the-model-is-not-the-product-mean-for-product-managers/
  2. User outcome: What should the user be able to do or achieve after completing this workflow? How is success measured?
  3. Contextual constraints: What assumptions or environmental factors shape user expectations and behavior (e.g., timing, information access, skill level)?

The best way to capture this is as a user story or problem scenario, not as a nebulous accuracy metric on a model. For example:

“When a financial analyst at PayPal reviews monthly revenue reports, they want to quickly spot anomalous transaction patterns so they can flag potential fraud before escalating to compliance.”

See how that defines:

  • User trigger: Reviewing monthly revenue reports
  • Outcome: Quickly spotting anomalies for fraud
  • Context: Workflow speed must match compliance timelines

Without this, AI is just a solution looking for a problem.

2. Define an Eval Before Release: What Does Success Look Like?

Once you have a workflow definition, it’s critical to design your evaluation upfront— before shipping or even building models. This avoids the trap of vague “accuracy improved” claims and gives your team a concrete standard for shipping decisions.

Use lightweight spreadsheet-based eval tools, leveraging metrics compatible with your user outcome and workflow. For example, from the PM Toolkit suite, you might consider:

  • Conversion rate: What portion of users complete the workflow successfully?
  • Activation rate: How many users start the workflow after the trigger?
  • NPS or PMF score: How satisfied are users with the workflow?
  • Cycle time or velocity: How long does the workflow take from trigger to outcome?
  • Retention and churn: Does the new workflow improve ongoing engagement?

Also consider statistically rigorous definitions for A/B testing. The PM Toolkit’s calculators for sample size and impact/effort scoring help you prioritize and validate changes systematically.

For example, before building AI to automatically flag fraudulent transactions, you might create spreadsheet-based evals for:

  1. Baseline false positive and false negative rates based on analyst alerts
  2. User acceptance thresholds (e.g., maximum false positives allowed to keep trust)
  3. Expected improvements in detection speed
  4. Estimated ROI based on reduced fraud losses and analyst time

H2O.ai teams often use this approach to surface key failure modes and tune models to business-relevant thresholds, not just academic accuracy.

3. Task-Dependent Model Behavior and Failure Modes

AI model behavior is almost always contingent on the task and user context. A single metric like “overall accuracy” obscures these nuances. Instead:

  • Segment your evaluation: Identify different sub-tasks or input conditions where model performance varies.
  • Surface failure modes: What kinds of errors are most problematic (e.g., hallucinations, misses, false alarms)?
  • Map errors to workflow impact: How does each failure mode affect user trust and outcomes?

For example, a natural language processing model might do well on straightforward questions but hallucinate on ambiguous inputs. PayPal’s risk teams, for instance, segment alerts by transaction type and severity, tailoring follow-up workflows accordingly.

Using spreadsheets for these segmented evals is valuable. You can cross-tab model outputs against real-world cases, identifying patterns of pain points. Without this, you risk shipping models that generate frustrating or even dangerous automated decisions.

4. Automation vs Augmentation vs Innovation Patterns

Your ultimate product design depends on whether your AI workflow is intended to:

  • Automate: Fully replace manual steps for time or cost savings
  • Augment: Assist users, enabling better or faster decisions
  • Innovate: Create new capabilities not possible before

Before you build, clarify which pattern fits your use case. The PM Toolkit ROI and business case calculators help quantify the trade-offs and ROI expectations for each pattern.

Pattern User Trigger Typical Outcomes Evaluation Focus Common Failure Concerns Automation Routine, repetitive tasks Task completed end-to-end without user intervention Accuracy, reliability, error rate Failures cause rework or loss of trust Augmentation Complex decisions needing human judgment User decision improved or sped up User satisfaction, decision quality, time saved Bias, confusing outputs, increased cognitive load Innovation New problems or user needs New capabilities unlocked, adoption metrics Adoption rate, retention, competitive differentiation Overpromising, unmet expectations

Understanding these patterns prevents premature scaling of AI bets that may only make sense as augmentations, or rushing innovations before problem clarity.

5. Using PM Toolkit Calculators and Spreadsheets to Ground Your Workflow Validation

The PM Toolkit provides an impressive set of calculators to support every stage of product analysis—perfect for AI workflow validation. Here’s how I integrate them:

  1. TAM/SAM/SOM: Estimate market opportunity for the workflow to check if it’s worthwhile.
  2. ROI and business case: Quantify potential savings or revenue impact from automation or augmentation.
  3. Pricing and MRR/ARR: Align workflow benefits to monetization models.
  4. Retention, churn, LTV, CAC: Evaluate how the workflow influences long-term user economics.
  5. RICE, ICE, Kano: Prioritize workflow features or AI capabilities with rigor.
  6. A/B test and sample size calculators: Plan validation experiments to prove impact pre-release.
  7. NPS and PMF score: Measure qualitative and satisfaction signals from early users.
  8. Velocity, cycle time, conversion rate: Assess how the AI workflow adjusts operational KPIs.
  9. DAU/MAU and activation rate: Track user engagement and behavioral shifts post-launch.

Alongside these, I always maintain lightweight evaluation spreadsheets that document example inputs, expected outputs, and user acceptance criteria. This spreadsheet acts as a living contract for the entire team — engineering, product, and design.

Summary: Workflow Definition Is the Foundation

Before writing AI models, double down on your workflow definition:

  • Be crystal clear on user trigger and outcome.
  • Design a robust evaluation plan upfront using real-world metrics.
  • Account for task-dependent behavior and failure modes through segmented evals.
  • Understand whether you are automating, augmenting, or innovating — and adjust validation accordingly.
  • Leverage proven frameworks like PM Toolkit calculators and maintain eval spreadsheets to ground decisions.

Otherwise, you risk spending months optimizing a model that sits unused or worse, harms workflow trust and productivity once deployed.

If you want a concrete starting point, I highly recommend exploring PM Toolkit’s calculators alongside your initial workflow interviews and existing user patterns. Companies like PayPal and H2O.ai have shown that rigorous upfront definition, combined with strategic evaluation, is the key to sailing past the hype and delivering AI-powered products users actually rely on.