Business Strategy&Lms Tech
Upscend Team
-February 2, 2026
9 min read
This article gives a week-by-week, 90-day plan to run a learning analytics pilot in enterprises. It covers discovery, minimal data mapping, simple models, dashboard builds, launch practices, KPIs and templates (RACI, data checklist). Use explainable models, small samples, and manager playbooks to prove value and prepare for scale.
learning analytics pilot projects are the fastest route to demonstrable value from L&D and procurement investments. With a focused, project-managed approach you can move from concept to measurable outcomes in 90 days. This guide provides a step by step learning analytics pilot plan for enterprises, with weekly objectives, technical checklists, stakeholder templates, and clear success criteria.
From pilots across sales, compliance, and product teams we've learned concise scope, early stakeholder alignment, and a data-first integration sprint increase the chance of scaling the learning analytics rollout. Typical pilots deliver measurable impact within the 90-day window—engagement lift, improved mastery rates, and reduced time-to-competency are realistic outcomes.
To convince procurement and executives, frame outcomes as operational savings (reduced ramp time) or risk reduction (audit readiness). That framing helps when you explain how to implement ai pilot workstreams and what a successful 90 day ai pilot looks like.
Below is a practical weekly breakdown for a 90 day ai pilot. Each week has focused deliverables and owners to maintain momentum.
Week 1: Kickoff—define sponsor objectives, select pilot cohort, and finalize success criteria. Use a one-page value hypothesis linking learning outcomes to business metrics (e.g., reduce support ticket volume by X% through faster product training).
Week 2: Inventory learning assets, systems, and user flows. Map events that matter (course start, completion, quiz) and document integration touchpoints (LMS APIs, HRIS exports, SSO logs). Estimate time-to-access for each source.
Week 3: Agree data access, privacy, and security. Finalize roles (RACI) and procurement checkpoints. Obtain legal sign-off on data sharing and confirm encryption, retention, and anonymization before any extracts.
Week 4: Create the data checklist and extract a representative sample. Limit scope to the smallest useful dataset (100–1,000 learners) to accelerate iteration. Stratify sampling by role or tenure.
Week 5: Ingest and validate data; implement basic ETL. Run quality checks on timestamps, user IDs, and assessment mappings. Set a refresh cadence for the pilot dataset (daily or nightly) so models and dashboards receive timely signals.
Week 6: Produce the first analytical dataset and prototype visualizations for stakeholder review. Include cohort comparisons and hand off an issues log to IT for missing fields or identity resolution gaps.
Week 7: Choose a hybrid approach—rules for early warning plus lightweight ML (logistic regression or decision tree) for explainability. Document acceptable error margins tied to pilot decisions.
Week 8: Train models on the small dataset, validate with cross-validation, and set baseline metrics (engagement, mastery, time-to-competency). Provide confusion matrices and calibration notes in governance documents to contextualize probabilistic outputs.
Week 9: Prepare model governance notes and rollout plans for expanded data. Define drift detection thresholds, re-training cadence, and owners for performance alerts.
Week 10: Build interactive dashboards showing cohort performance, risk flags, and intervention suggestions. Prioritize mobile-friendly views for managers who act on insights on the go.
Week 11: Conduct user acceptance testing with L&D and frontline managers; refine labels, filters, and exportable lists. Capture representative use cases so dashboards support real decisions.
Weeks 12–13: Finalize training materials and short playbooks with scripts for outreach and a one-page intervention checklist to reduce friction for busy managers.
Week 14: Launch to the selected cohort, run daily checks, and collect qualitative feedback via short surveys and manager interviews. Surface usability issues and immediate data problems.
Week 15: Implement prioritized fixes and measure against predefined success criteria. Produce a concise pilot report for the executive sponsor with wins, risks, and recommended next steps for the learning analytics rollout.
Data is the most common blocker. To implement ai pilot processes quickly, insist on a minimal, well-documented dataset that supports your use cases. Avoid solving full identity resolution and historical backfills in the pilot—those come during scale.
Start with a CSV extract or API pull from LMS and HRIS, and create a mapping document tying system events to analytical events. Define transformation rules and a single source of truth for learner identity. For privacy, hash PII and store reversible mappings only in IT-controlled environments.
Run three quick validation scripts (uniqueness, null checks, range checks) and include synthetic data generation to test dashboards without exposing real user data. Teams that formalize mapping and run these checks typically complete integration faster.
Select models that balance accuracy with explainability. For most enterprise pilots the hybrid approach—rules for early alerts and simple ML for probabilistic risk scoring—produces understandable outputs and rapid confidence. Keep complexity low to reduce governance friction.
Prioritize these dashboard views:
Visualization best practices speed adoption: clear filters, contextual tooltips, exportable lists, and a one-click "explain" action that surfaces top factors driving a learner's score. Log intervention outcomes so models can learn from manager actions—closing the loop increases pilot value.
Launch with a tight feedback loop: daily monitoring in week one, then weekly sprints for improvements. Assign one product owner and one analytics engineer to respond to issues within 48 hours. Track adoption KPIs such as dashboard logins, manager interventions, and follow-up learner improvements.
Quick feedback, clear ownership, and visible impact are the three factors that convert pilots into scaled programs.
Change management should include manager playbooks, short training sessions, and a communications calendar. Expect resistance early and plan targeted outreach to managers with high-impact learners. Use A/B messaging to test which communications convert managers to active users.
To maintain executive buy-in, present a concise dashboard showing uplift projections and early wins (e.g., reduced time-to-competency week-over-week). That converts pilot metrics into an investment case and answers how to run a learning analytics pilot in 90 days with concrete evidence.
Use these condensed templates to accelerate setup—copy them into project docs and refine in week one.
| KPI | Baseline | Pilot Target (90 days) |
|---|---|---|
| Engagement rate | 45% | 60% |
| Mastery rate | 62% | 75% |
| Time-to-competency | 28 days | 21 days |
Attach rationale for each KPI so executives see how targets map to business outcomes (e.g., a 7-day reduction in time-to-competency equates to X% faster productivity and Y cost savings).
A regional sales team needed faster ramp. The pilot focused on product modules, call simulations, and manager coaching logs. Within 90 days the learning analytics pilot identified low-performing modules, recommended targeted microlearning, and reduced average ramp time by 20%, translating to faster quota attainment and fewer shadowing hours. Key tactics: cohort selection, weekly manager reports, and rapid content fixes. Track downstream metrics (call conversion, demos booked) to attribute learning impact to revenue.
A compliance team required predictable completion before audits. The pilot integrated LMS completions with manager attestations and used a rules engine to flag late completions. The pilot achieved 95% on-time completion for the cohort and automated audit reporting, cutting audit prep time by an estimated 60%. Key tactics: automated reminders, manager escalation rules, and a compliance-ready export.
Running a successful learning analytics pilot in 90 days is feasible with disciplined scope, a minimal viable dataset, and clear ownership. Follow the week-by-week plan, use the templates, and prioritize explainable models and manager workflows to ensure adoption. Whether you're looking to implement ai pilot approaches or learn how to run a learning analytics pilot in 90 days, this plan reduces ambiguity and accelerates value.
Start by drafting the RACI and data checklist in week one, extract a small dataset in weeks four to six, and focus on actionable dashboards in weeks ten to thirteen. For quick proof-of-value, run parallel small cohorts (e.g., sales and compliance) to demonstrate cross-functional impact and build momentum for a broader learning analytics rollout.
Next step: Schedule a 60-minute planning workshop with procurement, L&D, IT, and a
