Your Chief Virtual Learning Architect

Learning to Performance

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Custom Design & Delivery

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Predictive Evaluation

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Enterprise Learning Strategy

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Dave Basarab Consulting has elevated the training experience, offering an end-to-end comprehensive approach that includes training strategy, instructional design, development, delivery , post program training transfer, and evaluation (via the unique Predictive Evaluation methodology). Virtual Chief Architect Dave Basarab has combined all of these individual training elements with his user-friendly, comprehensive Learning to Performance approach, which significantly increases companies' training ROI.

Why we are different

Innovation: Our primary focus is creating learning programs using a Learning to Performance approach.
Expertise: Dave offers the depth and breadth of his experience, including working internally at prestigious companies (Motorola, Ingersoll Rand and Pitney Bowes) as well as his knowledge and expertise as a highly-respected, sought-after consultant.
Partnership: Basarab collaborates with companies, serving as their own Chief Learning Officer whenever they need to plan, strategize, or implement training initiatives.

Training Services

Enterprise Learning Strategy

Enterprise Learning Strategy

At Dave Basarab Consulting, we're experts in creating Enterprise Learning Plans. We work with you to develop your company's learning strategy and direction.

Custom Design, Development, & Delivery

Custom Design, Development, & Delivery

Custom training is an effective way of developing the capability required to execute your strategy. We are a custom design house that creates programs specific to your business.

Learning Burst Development

Learning Burst Development

We can create and deliver your courses via our unique Learning Burst Method - keeping your employees at their jobs while receiving world-class training.

Predictive Evaluation

Predictive Evaluation

We predict the ROI for your courses and establish success gates. We then evaluate the course against the success gates to show value realized and continually improve results.

Leadership Development

Leadership Development

Turn-key virtual custom leadership development program that combines world-class leadership speakers/educators with post-event personalized coaching to provide you with a cadre of highly skilled leaders.

Predictive Evaluation

Predictive Evaluation in a Major Digital Transformation

A major digital transformation can fail long after the technology goes live. Employees may complete training, pass assessments, and still return to familiar processes. The organization then owns an expensive platform without achieving the productivity, customer service, or compliance improvements used to justify the investment.

This case study examines an anonymized global manufacturer that replaced fragmented systems with an integrated cloud platform. The program affected approximately 18,000 employees across sales, operations, finance, and customer support. Its learning team used Predictive Evaluation to connect capability building with adoption, performance, and financial outcomes.

The approach changed evaluation from a reporting exercise into a decision system. Instead of waiting for annual results, leaders received early signals about whether employees understood the new workflows, intended to use them, and could apply them consistently under operating pressure.

Business Context And Stakes

The transformation introduced a common process model, new analytics dashboards, and automated approval workflows. Each business unit had different levels of digital maturity, so a single completion rate could not show whether the change was taking hold. Some teams completed every required course while continuing to rely on spreadsheets and manual workarounds.

The executive sponsor set three measurable outcomes: reduce order-processing time, increase use of the new platform, and improve data accuracy. Learning leaders were asked to demonstrate how training influenced those outcomes. They also needed to distinguish learning problems from issues involving system usability, manager reinforcement, process design, or conflicting performance incentives.

This distinction made the evaluation credible. Training was treated as one part of a performance system rather than the automatic explanation for every business result.

Building The Predictive Evaluation Model

The project team began by mapping business goals to observable employee behaviors. For customer support, the critical behaviors included recording interactions in the platform, using standardized case categories, and resolving requests through the new knowledge workflow. For managers, the focus included reviewing adoption dashboards and coaching employees during weekly operating meetings.

The team then defined leading, intermediate, and lagging indicators. Leading indicators included practice accuracy, confidence, manager reinforcement, and access to job aids. Intermediate indicators included workflow usage, data completeness, and cycle-time changes. Lagging indicators included customer resolution time, rework, and operating cost.

This logic aligned closely with an enterprise learning strategy, because the learning portfolio was prioritized according to business capability rather than course volume. Each measure had an owner, a collection method, and a decision rule.

Establishing A Baseline

Before launch, the organization collected baseline data from system logs, employee interviews, manager surveys, quality audits, and operational reports. The baseline showed that only 42% of support cases followed the intended process, while average resolution time was 31 hours. Employees understood the strategic reason for the transformation, but many lacked confidence in the new workflow.

Predictive Evaluation combined these findings into adoption forecasts for each business unit. A forecast was not presented as a guarantee. It identified the probability that a group would demonstrate the target behavior within a defined period, based on capability, motivation, opportunity, and reinforcement.

Evaluation Stage	Evidence Collected	Early Signal	Management Decision
Readiness	Interviews, baseline skills, manager capacity	Risk of weak preparation	Add role-based practice and manager briefings
Learning transfer	Simulations, confidence, coaching activity	Risk of low workplace application	Provide job aids and targeted coaching
Adoption	Workflow usage, data quality, exception rates	Risk of reverting to old processes	Remove process barriers and reinforce standards
Business impact	Cycle time, rework, customer outcomes	Progress toward value case	Scale, adjust, or redesign the intervention

After the first pilot, the model predicted that two regions were likely to meet the adoption target and three were at risk. The risk was not caused by low course attendance. Those regions had limited manager involvement and unusually high workloads, which left little time for practice.

Turning Evidence Into Decisions

The evaluation team created a monthly review with business sponsors, technology leaders, operations managers, and learning professionals. Each meeting focused on decisions: where to increase support, which barriers to remove, and whether the adoption forecast had changed.

In one region, knowledge assessment scores were high, but system usage remained low. Interviews revealed that supervisors were still requesting reports in the old format, signaling that the local performance environment contradicted the training. Leaders changed the reporting routine and added workflow checks to team meetings. Usage increased during the following measurement period.

The analysis also prevented an inappropriate training response. A rise in data errors came from confusing field labels in the platform, not from employee misunderstanding. The technology team corrected the interface while learning designers created a short reference guide. This separation of causes reduced wasted effort and improved trust in the evaluation process.

Supporting Adoption At Scale

The organization used targeted learning bursts during the first 90 days after rollout. Each burst addressed one behavior, such as completing a case record correctly or using the dashboard to prioritize work. The content was delivered through short demonstrations, scenario practice, manager prompts, and follow-up checks.

This use of learning bursts allowed the team to respond to evidence instead of releasing a large volume of generic refresher content. Bursts were assigned to groups showing a specific adoption risk, while high-performing teams received peer-sharing opportunities.

The evaluation showed that manager reinforcement was a stronger predictor of sustained usage than employee confidence alone. As a result, supervisors received conversation guides and weekly indicators that helped them coach observable behaviors. Learning support became part of the operating rhythm rather than a separate event.

Practical Lessons For Learning Leaders

The case produced several principles that can guide enterprise transformation programs:

Define business behaviors before selecting courses or evaluation instruments.
Establish baseline performance before the intervention begins.
Combine learning data with operational, system, and manager evidence.
Use forecasts to trigger timely action, not to assign blame.
Review evaluation findings with the leaders who can change the work environment.

A further lesson involved leadership development. Sponsors initially wanted a satisfaction score for the leadership track, but the team focused instead on whether leaders reinforced new behaviors and removed barriers. This avoided the leadership evaluation mistakes that can make development metrics look positive while workplace behavior remains unchanged.

By the end of the first year, platform usage had reached 86%, average resolution time had fallen to 22 hours, and data completeness had improved substantially. The results could not be attributed to training alone. Process redesign, technology fixes, manager action, and learning interventions worked together. Predictive Evaluation made those relationships visible and helped leaders invest where the evidence showed the greatest need.

Organizations preparing for a digital transformation can apply the same discipline by connecting learning measures to business outcomes before launch. Dave Basarab Consulting helps leaders design enterprise learning strategies, custom learning experiences, and Predictive Evaluation systems that show whether capability is becoming measurable performance. Begin the evaluation design before the first course is assigned, and make adoption part of the transformation plan from day one.

Learning to Performance

Learning to Performance, a complete training approach, gives companies world-class training to drive significant return. This approach includes upfront work (Impact Mapping, design), training (for staff and company executives), and post-training efforts to ensure training transfer. This unique recipe - the key for successful training and adoption - is changing the way companies implement training. This methodology could work with any content for organizations in any industry.