SoloHarness

Trainers · Corporate learning and workplace application

Connect course exercises to the business,
and bring learning back to the workplace.

SoloHarness provides agent consulting, training, and ongoing implementation support for trainers and corporate training teams. Together, we turn business goals into learning objectives, turn job tasks into cases, exercises, and feedback forms, and then use on-the-job application records to continuously improve courses.

Talk about your industry and AI transformationView the six training milestones and specific tasks ↓

Step 3 example · Course and exercise design

"Trainees understood it, but back on the job they didn't know how to apply it."

Business objectives → role tasks
→ Situational practice → Application at work

  1. Before design:Trainers and supervisors break business goals into role-based tasks and confirm which behaviors can be improved through learning.
  2. During practice:Turn confirmed work scenarios into cases, role cards and deliverable templates; AI organizes the materials, trainers check the difficulty and boundaries.
  3. After class:Use learning artifacts, supervisor observations and job outputs to connect feedback and iteration, so the next course round stays closer to real tasks.

From business goals to continuous iteration

Six nodes connecting the complete training value flow.

Expand each node to view its purpose, three tasks, capabilities, AI support, work outputs, and changes in quality and efficiency.

The three-way relationship in the same training task

Business value flow, professional capability network and technology stack support each other.

The value flow explains how training moves from business goals to job application; the capability network connects needs analysis, instructional design, facilitation feedback and transfer support; the technology stack handles transcription, retrieval, structuring and reminders within specific tasks. Trainers judge context, standards and feedback, while AI supports preparation, practice and evidence organization.

01Training value flow stages

Business objectives and stakeholder analysis

Expand tasks +

Translate business goals into the job tasks, behavioral boundaries and observable outputs that training must serve.

Work inputs · Business plans, role materials, participant and resource information

1.1

Clarify business goals and position in the value chain

Reusable capabilities
Requirements analysis, value flow understanding, problem structuring, stakeholder communication
AI support and professional confirmation
Transcribe interviews and extract goals, constraints, metrics and owners, building a "business results—role behaviors—learning content" relationship table; trainers confirm wording, priorities and scope, and business owners confirm ownership.
Work outputs
Business goal–role behavior mapping table, scope statement, interview notes
Quality changes
The connection between goals and training content is clearer, and responsibilities and prerequisites are traceable
Efficiency changes
Reduce interview note organization, goal consolidation and repeated cross-checking
1.2

Analyze role tasks and work scenarios

Reusable capabilities
Role analysis, process breakdown, on-site observation, control object identification
AI support and professional confirmation
Parse job descriptions, process documents, and case studies, extracting tasks by "trigger—operation—judgment—output—handoff," and present the relationships among roles, systems, and owners; trainers correct on-site differences, and supervisors confirm priorities and exception boundaries.
Work outputs
Role task map, scenario list, deliverable samples, boundary records
Quality changes
Role context, task requirements and handoff conditions are more complete
Efficiency changes
Reduce manual assembly of role workflows from scattered materials
1.3

Define trainee audience and training constraints

Reusable capabilities
Audience analysis, resource planning, constraint management, communication and coordination
AI support and professional confirmation
Link registration forms, historical learning records and supervisor materials by authorized fields, verify grouping conditions, time conflicts and resource gaps, and generate candidates for online, offline or hybrid arrangements; trainers confirm grouping, interaction conditions and psychological safety, while organizers confirm scheduling, equipment and permissions.
Work outputs
Trainee tiering table, training constraints table, resource list, scheduling draft
Quality changes
Learner fit and delivery conditions are clearer, and design assumptions are traceable
Efficiency changes
Reduce repeated confirmation of registration information, scheduling and resources

Advancement and handover · After the business owner confirms the goals, targets, scope and resources, move into requirements research.

02Training value flow stages

Learning needs and capability gap research

Expand tasks +

Identify gaps between job requirements and current performance that can be improved through learning, and distinguish them from process, tool and management issues.

Work inputs · Interviews, role standards, current task outputs and observation records

2.1

Collect evidence of needs from supervisors and trainees

Reusable capabilities
Interview listening, evidence collection, needs analysis, deviation identification
AI support and professional confirmation
Generate interview outlines and follow-up questions, transcribe and classify by role, task, difficulty, cause and example, and flag statements that are contradictory or lack a source; the trainer chooses the follow-up strategy, and the manager and trainee confirm the facts and priorities.
Work outputs
Requirement interview notes, evidence index, differing-viewpoints table
Quality changes
Requirements are grounded closer to real work, making preferences and problems easier to distinguish
Efficiency changes
Reduce time spent on transcription, coding, comparison, and preparing follow-up questions
2.2

Analyze capability gaps and their causes

Reusable capabilities
Capability modeling, root cause analysis, evidence judgment, prioritization
AI support and professional confirmation
Map job tasks and observation records to capability items, extract structured gaps from cases and assignments, and generate analysis drafts based on "can they do it, can they do it consistently, can they transfer it, are they limited by the environment"; trainers confirm evidence sufficiency, managers confirm job standards and non-training factors.
Work outputs
Capability gap matrix, root cause classification, training priorities
Quality changes
Record learning gaps separately from tool, process and management factors
Efficiency changes
Reduce manual comparison against criteria, problem classification and consolidation of common gaps
2.3

Form observable learning objectives

Reusable capabilities
Objective design, behavior description, difficulty tiering, acceptance criteria design
AI support and professional confirmation
Generate target candidates based on the task map and gap matrix, check whether they include scenario, action, deliverable and quality conditions, and layer them by basic, proficient and complex situations; the trainer chooses the wording and evidence method, and the business owner confirms the target job tasks.
Work outputs
Learning objectives list, objective–task–evidence mapping
Quality changes
Goals are more observable and assessable, with a clearer relationship to role tasks
Efficiency changes
Reduce time spent revising goal wording and aligning item by item

Advancement and handover · Trainers and supervisors confirm gap evidence and learning objectives before entering course design.

03Training value flow stages

Course and exercise design

Expand tasks +

Turn learning objectives into a learning plan with sequence, context, practice and feedback mechanisms.

Work inputs · Learning objectives, authorized cases, participant baseline and training duration

3.1

Organize course structure and learning paths

Reusable capabilities
Content organization, instructional design, cognitive load management, path planning
AI support and professional confirmation
Generate a course skeleton based on objectives, learner baseline and duration, check for duplication, prerequisites and objective coverage, and recall confirmed concepts, processes and cases from the knowledge base; the trainer decides the order, deletions and time allocation, and the business expert confirms the professional boundaries.
Work outputs
Course outline, objective coverage matrix, class schedule
Quality changes
Clearer correspondence between goals, content, activities and timing
Efficiency changes
Reduce time spent on outline drafts, content consolidation and coverage checks
3.2

Design role cases and exercises

Reusable capabilities
Case design, exercise design, scenario simulation, risk and permission control
AI support and professional confirmation
De-identify authorized cases and generate scenario variants; generate practice instructions, role cards, material packs, and common mistakes by objective; check inputs and difficulty with a rules checklist; trainers confirm authenticity and teaching intent, while business staff confirm permissions and the acceptable answer range.
Work outputs
Case packages, practice task sheets, role cards, deliverable templates
Quality changes
Practice fit, difficulty gradient and evaluation criteria are clearer
Efficiency changes
Reduce case rewriting, material arrangement and multi-version production
3.3

Design the mechanism connecting feedback, evaluation and role application

Reusable capabilities
Feedback design, learning assessment, transfer design, collaboration mechanism design
AI support and professional confirmation
Based on the objective number and evaluation criteria, generate observation scales, feedback prompts, assignment checklists and follow-up templates, linking classroom evidence, on-the-job behavior evidence and business observation items via relational tables; the trainer confirms the applicable evaluation definitions and criteria and manual observation points, and the manager confirms observation timing and data definitions and criteria.
Work outputs
Feedback forms, assessment plans, role application task sheets, collection plan
Quality changes
Clearer boundaries between training feedback, learning performance, job behavior and business results
Efficiency changes
Reduce form creation, feedback consolidation and missed follow-ups

Advancement and handover · The trainer confirms target coverage and activity feasibility, and the business expert confirms the professional materials before teaching begins.

04Training value flow stages

Training & Drills

Expand tasks +

Guide understanding, practice, feedback and correction in real or simulated situations to form observable learning performance.

Work inputs · Course package, practice environment, group arrangements and observation criteria

4.1

Prepare teaching materials and on-site process

Reusable capabilities
Information verification, process orchestration, resource coordination, risk anticipation
AI support and professional confirmation
Generate teaching scripts, material lists, time reminders and FAQ cards based on the course version, and check file versions, links, fields and case inputs; the trainer confirms professional content and on-site trade-offs, the organizer confirms equipment, personnel and permissions.
Work outputs
Course package, on-site run sheet, grouping and schedule, contingency plans
Quality changes
Material versions, event coordination and on-site responsibilities are clearer
Efficiency changes
Reduce pre-meeting checks, version hunting and last-minute preparation
4.2

Teach concepts and connect them to job tasks

Reusable capabilities
Instruction and facilitation, audience adaptation, concept explanation, interactive questioning
AI support and professional confirmation
Generate different explanations, examples and follow-up questions based on learner roles, retrieve definitions, processes and sources from the authorized knowledge base, aggregate anonymous questions and flag unanswered topics; trainers choose the explanation approach and address misunderstandings, while business experts handle professional boundary questions.
Work outputs
Class lecture notes, question lists, concept–task mapping notes
Quality changes
Clearer connection between concept understanding and job context
Efficiency changes
Reduce on-site searching, repeated answers and post-session follow-up explanations
4.3

Organize practice, observe performance and give immediate feedback

Reusable capabilities
Practice facilitation, observation feedback, performance analysis, on-site adjustment
AI support and professional confirmation
Read learner outputs by learner ID, exercise number and observation criteria, validate fields and extract textual evidence, flag missing items, contradictions and common errors, and generate feedback candidates with source citations; the trainer confirms feedback priorities, context and performance conclusions, and the learner decides how to revise.
Work outputs
Practice outputs, observation records, immediate feedback, revised versions
Quality changes
Feedback is closer to specific behaviors and outputs, making the correction path clearer
Efficiency changes
Reduce the work of organizing exercises one by one, recording common errors, and writing feedback

Advancement and handover · The trainer confirms the required learning evidence has been obtained before moving to assessment; if practice opportunities are insufficient, additional practice is arranged.

05Training value flow stages

Learning effectiveness evaluation

Expand tasks +

Organize course experience and learning performance separately, assess mastery based on goals and actual outputs, and form a plan for on-the-job application.

Work inputs · Training feedback, quizzes, work samples, and observation records

5.1

Organize training feedback and implementation experience

Reusable capabilities
Survey design, information categorization, observational feedback, problem analysis
AI support and professional confirmation
Collect feedback approved for use and deduplicate it by training cohort and course version. Group comments by topic with links to the originals, and calculate valid response counts, missing answers and response distributions. The trainer checks representativeness and context; the organizer confirms who owns each improvement.
Work outputs
Feedback topic table, valid feedback statistics, implementation improvement checklist
Quality changes
Experience issues map to specific steps and original feedback
Efficiency changes
Reduce exporting, deduplication, classification and excerpting
5.2

Assess understanding and task performance

Reusable capabilities
Learning assessment, evidence judgment, criteria application, performance analysis
AI support and professional confirmation
Retrieve work by trainee, course version and objective number, extract evidence and gaps against defined evaluation criteria, and generate item-by-item review suggestions; the trainer confirms scoring rationale, complex situational judgments and final comments, and the trainee may add explanations or present again.
Work outputs
Goal–performance–evidence evaluation form, manual confirmation results, practice list
Quality changes
Learning conclusions linked to specific performance and standards
Efficiency changes
Reduce work in portfolio search, rule calculation and preliminary review organization
5.3

Form learning review and application readiness judgment

Reusable capabilities
Comprehensive analysis, performance analysis, feedback communication, action planning
AI support and professional confirmation
Link goals, work, observations, and feedback to generate layered reviews and practice candidates, check whether conclusions have evidence citations, and create confirmed coaching tasks; the trainer decides the coaching content and application conditions, the supervisor confirms work opportunities and risk boundaries, and the learner confirms the action plan.
Work outputs
Learning review, supplementary practice and coaching plan, role application handover form
Quality changes
Assessment conclusions connect with next-step support, making application decisions better grounded
Efficiency changes
Reduce cross-sheet consolidation, one-on-one coaching scheduling and handover data entry

Advancement and handover · The trainer confirms learning performance, and after the supervisor confirms the application conditions, job practice is arranged; weak objectives go into coaching.

06Training value flow stages

Track workplace application and improve training

Expand tasks +

Turn learning into on-the-job application records, identify transfer barriers, and use role-based evidence to drive continuous improvement in courses and on-site support.

Work inputs · Applied work, supervisor observation, learner retrospectives and business data

6.1

Establish role-based application tasks and support arrangements

Reusable capabilities
Behavior transfer, task design, collaboration planning, risk control
AI support and professional confirmation
Read confirmed learning performance, job tasks and supervisor arrangements to generate application task candidates; create tasks and reminders after supervisor approval through the calendar or project system, and filter materials by role; trainers select tasks suitable for transfer, supervisors confirm authorization and observation responsibilities, and learners confirm execution conditions.
Work outputs
Role application plan, task and reminder records, observation criteria
Quality changes
Application opportunities, responsibilities and evidence requirements are clear
Efficiency changes
Reduce repetitive work in task assignment, reminders and material handovers
6.2

Track role behaviors and transfer barriers

Reusable capabilities
Behavior observation, evidence verification, root-cause analysis, coaching feedback
AI support and professional confirmation
Query application records by person, job task, time and version, compare observations and work products against behavioral standards, extract adoption methods, difficulties and exceptions, and generate follow-up questions and coaching suggestions with sources; the trainer verifies transfer performance, the supervisor confirms real task conditions, and the learner explains the judgment process.
Work outputs
Role application evidence table, transfer barrier classification, coaching records
Quality changes
Actual behavior and self-reported feelings can be cross-checked
Efficiency changes
Reduce scattered follow-up organization, cross-system portfolio searching and repeated inquiries
6.3

Review business connections and iterate the curriculum

Reusable capabilities
Business analysis, evidence reasoning, continuous iteration, version management
AI support and professional confirmation
Read baseline and follow-up data according to the approved metric definitions and observation window, recalculate changes programmatically, correlate with role application records and organize concurrent factors to form course revision candidates and version differences; the business owner judges the business interpretation, and the trainer chooses course changes and the next round of validation methods.
Work outputs
Business linkage review, evidence index, course iteration checklist, new version plan
Quality changes
The relationship between business observations, behavioral evidence and course changes is more traceable
Efficiency changes
Reduce metrics organization, version comparison and review material preparation

Advancement and handover · Trainers and supervisors confirm obstacle handling, iteration content, and the next round of observation plans, then return to needs research to continuously improve the capability formation path.

Start with your actual training work

Turn the next course into sustainable job capabilities.

Starting from business goals, learner tasks and existing courses, identify capability enhancement opportunities, choose suitable AI and system connections, and continuously check learning outputs, job application and handover progress.

Talk about your industry and AI transformation

Contact Andy

Talk about your industry and AI transformation

Add me on WeChat and let's talk about the one piece of work you want to improve first.

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