Understand Before Automating
Automation should follow a clear understanding of the workflow, not hide a poorly designed process.
Product approach
A practical operating model for complex product decisions.
I use a structured but adaptable approach to identify high-leverage problems, align teams around explicit trade-offs and connect product decisions to measurable outcomes. The models and artifacts below demonstrate how I bring clarity to complex healthcare, automation and data-product environments.
- Strategic judgment
- Problem framing
- Prioritization
- Creative problem solving
- Systems thinking
- Cross-functional alignment
- Measurement discipline
Working product framework
Ola Sobo’s MIN–MAX Product Model
My working product framework for minimizing operational friction and maximizing measurable value. It is separate from the MIN–MAX Living multi-agent product system.
Product operating principles
Six principles for building trustworthy automation.
Design for Exceptions
Failure states, retries, overrides, and ambiguity must be designed intentionally.
Make Decisions Explainable
Users should understand why a product prioritized, routed, assigned, or recommended an action.
Measure Outcomes, Not Output
Adoption, reliability, efficiency, quality, and business performance matter more than feature volume.
Preserve Human Judgment
High-impact automation should improve decisions without removing necessary oversight.
Treat Data Quality as Product Quality
A product cannot be trusted when its underlying data is incomplete, inconsistent, or poorly understood.
Discovery to optimization
A product operating rhythm built around evidence.
Each phase produces a clearer decision—not simply more documentation.
AI-assisted product practice
Use AI to accelerate evidence—not outsource judgment.
AI accelerates execution; Ola retains human ownership of prioritization, product judgment, risk, validation and release decisions.
Accelerate product-management work
Synthesize stakeholder input, support research, accelerate prototypes, refine stories and acceptance criteria, generate edge cases, summarize feedback and draft documentation.
Design AI-agent workflows
Define specialized roles, operating instructions, handoffs, evidence requirements, evaluation, escalation boundaries and release-readiness controls.
Own AI-enabled product outcomes
Own the user problem, product architecture, decision rights, risk boundaries, validation and measurable outcome delivered through the system.
Preserve the technical boundary
Third-party foundation-model services may support execution. Ola does not claim to train, own or have built the underlying models.
Product artifacts
Artifacts as decision tools.
Fourteen fictionalized, public-safe specimens show the decisions each artifact enables—from roadmap sequencing and journey framing to workflow design, release control, risk and post-launch monitoring. Select any card to explore its distinct structure.
Let’s build what matters
Building a data-intensive, AI-enabled or healthcare workflow product?
Review the evidence or discuss how product strategy, API-driven workflow automation, AI-enabled product operations and technical fluency can support the decisions your team needs to improve.