How Redwood Performance Group Uses AI in Its Learning Solutions

Updated

Redwood Performance Group applies AI to corporate learning in four specific ways: personalized learning paths, AI-based skills gap analysis, learning analytics, and adaptive content delivery. The company describes itself as an early adopter of Generative AI (GAI) technologies and positions these capabilities as part of a broader learning and development (L&D) offering that also includes strategic performance consulting and full-service content production. If you are evaluating vendors for AI-enabled upskilling, the relevant question is whether these four functions map to problems you actually have — and the descriptions below give you the basis for that judgment.

The four AI capabilities Redwood describes

Personalized learning that adapts to each learner

Redwood states that it delivers "AI-powered personal learning solutions that adapt to each learner's needs." In practice, this means the learning experience is not a single fixed course sequence for everyone. The stated purpose is higher engagement and faster skill development — the logic being that learners spend time on content matched to their current level rather than on material they have already mastered or are not ready for.

AI-based skills gap analysis

Before training is delivered, Redwood uses AI to identify where skills gaps exist. This matters because it determines what gets taught. A gap analysis that runs on learner data rather than on manager opinion can surface differences across teams, roles, or locations that a single needs-assessment workshop would miss.

Learning analytics

Redwood pairs the gap analysis with ongoing learning analytics. The company frames this as part of a "measurable, data-driven ROI" story: learning initiatives are tied to organizational goals, and analytics provide the evidence of whether those goals are being met. For an L&D buyer, this is the difference between reporting completion rates and reporting capability change.

Adaptive learning

Adaptive learning is listed alongside the other three as a distinct area of expertise. It is the mechanism that connects the other pieces — the system adjusts content or pacing based on what the analytics and gap analysis show about each learner.

What problems these are meant to solve

Redwood's own framing ties the AI work to three outcomes:

Stated goal How the AI capabilities connect to it
Higher engagement Personalized and adaptive content keeps learners on material relevant to them
Faster skill development Gap analysis directs effort to actual deficiencies instead of broad curricula
Lower training costs Targeting reduces redundant or unnecessary training hours
Measurable ROI Learning analytics produce data tied to business goals

The cost and ROI claims are the company's positioning, not independently verified figures. Treat them as the vendor's stated objectives and ask for evidence specific to your organization during evaluation.

How this fits the rest of Redwood's offering

The AI capabilities are not a standalone product. Redwood describes itself as going "beyond traditional Learning and Development" by combining strategic performance consulting with full-service content production. That means AI-driven personalization and analytics sit on top of content the company can also produce, and inside a consulting engagement that aligns learning with business goals.

Two adjacent programs illustrate the range:

  • Leadership development — Redwood specializes in training agile team leaders, with the stated aims of smarter decision-making, improved employee satisfaction, and higher retention.
  • CareerForce — an immersive learning simulation built to develop human skills: communication, collaboration, adaptability, and critical thinking. It was developed in partnership with Canada's financial sector.

CareerForce is worth noting because it is a simulation rather than an AI-personalized course. It shows that Redwood's answer to skills development is not exclusively AI-driven — the AI layer supports a portfolio that also includes scenario-based and team-based training.

What to check before choosing this approach

If AI-enabled learning is what you need, these are the practical questions the page evidence does not answer and you should raise directly:

  1. Data requirements. Personalized learning and gap analysis depend on learner data. Ask what data Redwood needs, where it is stored, and how it is governed.
  2. Integration with your existing systems. Adaptive learning usually has to talk to an LMS or HR system. Confirm compatibility before committing.
  3. Baseline measurement. Analytics only demonstrate ROI against a starting point. Ask how the baseline is established.
  4. Scope of the AI claim. "AI-powered" can mean anything from recommendation engines to generative content creation. Ask which of the four capabilities above are actually in scope for your engagement.
  5. Pricing and access terms. The available site information does not state pricing, packaging, or login requirements. Request a quote and terms rather than assuming any tier is free or self-serve.

The clearest fit is an organization that already invests in L&D, has data to feed a gap analysis, and wants training tied to measurable business outcomes rather than delivered as a fixed catalog. If your need is a one-off course with no measurement requirement, the AI layer is likely more machinery than the problem calls for.