Pearson's Next Chapter: Omar Abbosh on AI, Customers, and Learning as a Skill

Omar Abbosh on Pearson's Hoboken Town Hall: Product lessons for AI-led learning

Pearson's CEO Omar Abbosh shared a clear shift from inward talk to customer-first, future-facing work during a town hall with employees in Hoboken. The focus: how AI reshapes product development and learner support, and how learning itself should be treated as a core skill.

Pearson builds digital learning tools, assessments, and content for students and professionals. The message to teams was simple: build what customers need next, and make experiences rigorous and cool.

Photo credit: Omar Abbosh

What product teams can use right now

• Anchor roadmaps to outcomes: Write clear learner and worker outcomes, not features. Track time-to-skill, completion, retention, and satisfaction over vanity metrics.
• Build AI into the lifecycle: Treat AI as part of discovery, prototyping, and launch. Create offline eval sets, red-team your features, and set policies for safety and bias. See the NIST AI Risk Management Framework for a solid baseline here.
• Treat learning as a skill: Ship features that build durable knowledge: retrieval practice, spaced repetition, formative checks, and feedback loops that meet users where they are.
• Raise the bar: "Rigorous and cool" is a useful test. Pair evidence-based pedagogy with interfaces that are fast, simple, and delightful to use.
• Instrument everything: Define success upfront, run small experiments, and publish weekly readouts. Kill or double-down based on data, not opinions.

Practical 90-day plan for product leaders

• Weeks 1-2: 10-15 customer calls; map top jobs-to-be-done; write a one-page POV doc; define 3 measurable outcomes.
• Weeks 3-6: Spike two AI concepts; build an offline eval set and human-in-the-loop review; document guardrails and failure modes.
• Weeks 7-10: Ship an alpha to 50-100 users; run A/B tests; review learning outcomes weekly; decide keep/iterate/kill.
• Weeks 11-12: Scale infra and support; update runbooks; prepare enablement for Sales, CS, and Support.

AI impact areas highlighted at Pearson

• Product development: Faster research synthesis, idea validation, and prototyping with structured evaluation.
• Learning experience design: Adaptive paths, retrieval-based checks, and feedback that builds confidence and competence.
• Learner and worker support: Context-aware help, better routing, and self-serve flows that reduce time to resolution.

Org moves to make this real

• Customer rhythm: Weekly user touchpoints; every PM and Designer ships one insight per week.
• Quality gates: Content and model evals before release; health metrics tracked in prod (accuracy, latency, satisfaction).
• Team fluency: Upskill PM, Design, and Eng on AI evaluation and prompt patterns; create shared templates for PRDs and experiment logs. If your team needs a starting point, see AI courses by job here.

Abbosh thanked the Hoboken team for their candor and energy, noting the shift to future-focused, customer-centered work. For product leaders, the takeaway is clear: tie AI to outcomes, raise the quality bar, and build fast feedback loops that make learning stick.