Is the training tailored to our business processes?

Yes. Every program starts with a training needs analysis (TNA) mapping participant roles, workflows, and AI maturity. Case studies, prompts, and exercises use your company's context, not generic examples. Instructional design follows the ADDIE approach so learning objectives are measurable and accountable.

How is data security handled when employees use AI?

The governance module is core, not an add-on. We help define a data policy, a list of information that must never enter public AI, and usage SOPs. The material is mapped to OWASP LLM Top 10 2025 (e.g. prompt injection, sensitive information disclosure) and aligned to UU PDP No. 27/2022 obligations (data controller/processor roles, DPO) and your internal security policy.

Is this training aligned with recognized AI governance frameworks?

Yes. The governance curriculum is explicitly mapped to NIST AI RMF (the Govern, Map, Measure, Manage functions and the GenAI Profile NIST AI 600-1), ISO/IEC 42001 (an AI Management System including AI impact assessment), OWASP LLM Top 10 2025, and UU PDP. The goal is not to claim certification but to prepare your teams and SOPs to align with these frameworks.

How is the ROI or impact of the training measured?

We use the Kirkpatrick model: Level 1 (reaction), Level 2 (learning via assessment), Level 3 (behavior change/adoption), Level 4 (business results). When finance needs a monetary figure, impact can be raised to Phillips ROI Level 5 with isolation of training effects from other factors. A baseline is set at the TNA so comparison is honest; we do not promise percentages we cannot prove.

What tooling and models are used during training?

We adapt to your stack: Microsoft 365 Copilot, Gemini for Workspace, ChatGPT Enterprise, or an internal LLM. If no license exists yet, we provide a safe temporary practice environment. Model selection criteria (data privacy, residency, cost, capability) are also discussed so your tooling decision rests on sound considerations.

Is in-house or public-class training better?

In-house wins when you want case studies from your own processes, organization-specific governance policy, and per-team impact measurement. Public classes suit early individual exploration. For companies with measurable adoption and compliance targets, in-house delivers relevance and control public classes cannot.

What is the ideal batch size?

8-30 participants per batch for hands-on workshops so each participant gets enough coaching. For large roll-outs the program splits into multiple batches on a rolling schedule, usually starting with a champion group on a 70-20-10 pattern before scaling.

How far can the program be customized per department?

Fully. After the TNA, modules, case studies, prompts, and exercises are tailored per function (marketing, operations, finance, HR, legal, IT). Outputs such as the prompt library and SOPs are built specifically for your department's context, not a uniform template.

Is there post-training support?

Yes. The ongoing program provides office hours, monthly adoption reviews, and on-the-job coaching on a 70-20-10 pattern (most learning happens in real work). A 30-60-90 day adoption plan is built together so momentum is not lost after the class ends.

Is there an evaluation report and certificate?

Yes. The report covers attendance, AI policy assessment scores, exercise results, and recommended next steps for the L&D team and leadership, mapped to Kirkpatrick levels. Every participant receives a certificate of participation. This completes the procurement requirement for proof of delivery.

Digital & AI Upskilling

Corporate Generative AI Training

Equip your teams to use generative AI productively and safely — from prompt engineering and RAG to agentic workflows — with governance mapped to NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, and Indonesia's PDP Law obligations.

format: In-house / online / hybrid
duration: 1-5 days or a 3-6 month ongoing program
participants: 8-30 per batch
language: Indonesian / English

Quick Answer

Corporate generative AI training is an in-house program that trains teams to use LLMs (ChatGPT, Copilot, Gemini) for real work — prompt engineering, RAG, agentic workflows — paired with governance mapped to NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, and Indonesia's PDP Law, then measured with the Kirkpatrick model.

Free Consultation

Indonesia's PDP Law (UU No. 27/2022) is now fully in force

Since October 2024 data controller & processor obligations are fully enforceable, with administrative sanctions up to 2% of annual revenue. AI training without a clear data SOP adds compliance risk. This program's governance module closes that gap from day one.

Governance mapped to recognized frameworks

The risk & security curriculum is explicitly mapped to NIST AI RMF (Govern/Map/Measure/Manage + the GenAI Profile NIST AI 600-1), ISO/IEC 42001, and OWASP LLM Top 10 2025 — not generic 'best practice'.

The most common mistake: teaching tooling without governance & measurement

Many programs stop at 'how to use ChatGPT'. Without a data SOP, use-case criteria, and an evaluation framework (Kirkpatrick/Phillips), adoption is unproven and data risk is uncontrolled. This program unifies skills, governance, and measurement.

Corporate Generative AI Training

Corporate generative AI training is an in-house program that equips employees to use large language models (LLMs) for real work — drafting, analysis, workflow automation, and internal assistants — paired with a governance framework mapped to NIST AI RMF, ISO/IEC 42001, and OWASP LLM Top 10, and aligned to Indonesia's PDP Law (UU No. 27/2022), so AI adoption is measurable, safe, and compliant.

1Role-designed via a training needs analysis (TNA): marketing, operations, finance, HR, legal, IT

2Case studies drawn from your own business processes, not generic samples

3Governance explicitly mapped to NIST AI RMF (Govern/Map/Measure/Manage), ISO/IEC 42001, OWASP LLM Top 10 2025, and UU PDP No. 27/2022

4Hands-on with the tooling you already run: Microsoft 365 Copilot, Gemini for Workspace, ChatGPT Enterprise, or an internal LLM

5Measured with the Kirkpatrick model (Levels 1-3), extendable to Phillips ROI (Level 5) when finance needs a monetized figure

6Measurable output: a prompt library, AI usage SOPs, and at least 2 production-ready use cases per department

Measurable Outcomes

Expected Outcomes

Success indicators mapped to Kirkpatrick/Phillips evaluation levels — qualitative targets, set jointly during the TNA.

AI tool adoption (Kirkpatrick L3 — Behavior): Most participants use AI in their weekly workflow within 30 days post-training
Document task productivity (L4 — Results): Reduced time on drafting, summarization, and repetitive analysis, measured against the team baseline
Governance compliance (L2 — Learning): All participants pass the AI policy & data guardrail assessment (aligned to OWASP LLM & UU PDP)
Internal use cases (L4 — Results): At least 2 production-ready use cases per department, documented with feasibility criteria
Prompt library & SOPs (L3 transfer): A validated prompt library plus AI usage SOPs approved by legal/security
Monetized ROI (Phillips L5 — optional): Net-benefit calculation with isolation of training effects, when finance requires a figure

Program Format

Program Format Options

Chosen by AI maturity, population size, and operational schedule — finalized after the TNA.

Guided Use-Case Workshop (1 day)

Focused session building 1-2 AI solutions for one team's real problem — problem framing, prompt design, output validation, and a guardrail checklist.

Best for: Teams with a concrete case wanting a measurable quick win

Fundamentals + Role-Based Bootcamp (2-3 days)

LLM basics, applied prompt engineering (zero/few-shot, chain-of-thought, ReAct), a governance module, and role-based case studies for large batches.

Best for: Initial cross-department roll-out

Agentic & RAG Intensive (3-5 days)

Deep dive into workflow automation, retrieval-augmented generation, internal assistants, and safe agentic boundaries — for technical/product teams.

Best for: IT, data, and product teams building internal AI solutions

Ongoing Program (3-6 months)

Phased training on a 70-20-10 pattern: formal classes, office hours, monthly adoption reviews, and on-the-job coaching.

Best for: Organization-wide AI transformation with behavior & impact targets

Free Consultation

Discuss your team's AI training needs

Start with a free training needs analysis: we map your roles, processes, and AI maturity, then build a proposal & budget estimate grounded in real needs.

Request Proposal

Curriculum

Curriculum Framework

Built with ADDIE; final modules curated from the TNA. Topics below are the full coverage that can be activated.

Comparison

Choosing a Program Format

A concise decision matrix — the final recommendation is set after the training needs analysis.

Aspect	Guided Workshop (1 day)	Fundamentals Bootcamp (2-3 days)	Agentic & RAG Intensive (3-5 days)	Ongoing Program (3-6 mo)
Primary goal	Quick win on 1-2 use cases	Broad literacy & adoption	Deep technical capability	Transformation & measured impact
Ideal participants	1 team with a concrete case	Many cross-functional teams	IT/data/product teams	Large population, phased
Governance depth	Basic use-case guardrails	AI policy module + UU PDP	OWASP LLM + agentic safety	Full SOP + institutionalization
Evaluation level	Kirkpatrick L1-L2	Kirkpatrick L1-L3	Kirkpatrick L1-L3	Kirkpatrick L1-L4 (+Phillips L5)
Best suited for	Validate fast before scaling	Initial organizational roll-out	Build internal AI solutions	Org-wide adoption & ROI targets

For Whom

Who Is This Program For?

Role-tailored via the TNA for direct relevance to daily work.

Knowledge workers (marketing, operations, finance)

Use AI to speed up repetitive tasks with reliable, safe output.

Common challenges

Sporadic, inconsistent AI usage across people
AI output not trustworthy without a verification method
Unsure what data may or may not be entered into public AI

Managers & team leads

Identify AI opportunities, judge use-case feasibility, and set team standards.

Common challenges

Hard to judge which use cases are viable and safe
No adoption guidance & no output quality standard
Concerned about compliance risk when teams use AI without rules

HR, L&D & People Development teams

Design measurable AI upskilling programs accountable to leadership.

Common challenges

Generic content not relevant to the company's processes
Hard to prove training impact to management/finance
No recognized evaluation framework (Kirkpatrick/Phillips)

IT, Data, Security & Legal/Compliance teams

Ensure AI adoption aligns with security policy, data privacy, and regulation.

Common challenges

Shadow AI: employees using tools without IT's knowledge
AI risks not yet mapped to NIST AI RMF / OWASP LLM
UU PDP obligations not yet translated into AI usage SOPs

Industry Context

Use Cases by Industry

One specific use case per industry, naming a real workflow & regulation in that vertical.

Banking & Financial Services

Credit document summarization & committee memo drafts, compliance QA, with customer-data guardrails aligned to UU PDP, OJK IT risk-management principles, and OWASP sensitive-information-disclosure controls — AI never touches confidential data on public channels.

Technology & Startups

Engineering assistant & PRD drafting, competitor research automation, RAG over internal documentation, with controls against prompt injection and source-code/secret leakage.

State-Owned Enterprises (BUMN)

Faster drafting of official notes, management reports, and board/RUPS materials, with an auditable governance trail aligned to UU PDP and ISO/IEC 42001 accountability principles for directors.

Manufacturing

Automated SOPs & manuals, shift report analysis, quality and deviation reporting assistant, with human validation before any quality document is used (improper output handling controlled).

Retail & FMCG

Campaign copy & channel variants, customer review sentiment analysis, service knowledge base, with brand-safety policy and customer data protection.

Government & Public Sector

Official letter drafts & policy summaries, public-service assistant, with restricted-information guardrails, decision transparency, and public-sector UU PDP compliance.

Delivery Method

Delivery

Format adapts to team distribution and operational schedule; every format is hands-on practice, not passive lecture.

In-house on-site

Facilitator comes to the office/company training venue; practice lab with company data & scenarios (in a safe environment).

Live online

Interactive class via Zoom/Teams with hands-on breakouts, screen-share review, and session recordings for participants.

Hybrid

On-site sessions for intensive practice & use-case labs, followed by online office hours for follow-up and adoption coaching.

Schedule built around the company's operational calendar & shifts

Materials, worksheets, and an initial prompt library prepared by the Neksus team

A safe practice environment if enterprise AI licenses are not yet available

Certificate of participation for every attendee

Post-training evaluation report for the L&D team & leadership

Engagement Flow

Engagement Path

From need to measured impact — qualitative durations, adapted to organization scale.

Training Needs Analysis (TNA)

Mapping roles, workflows, AI maturity, data policy, and business goals. Output: a needs profile + measurement baseline.

Initial stage

Program Design (ADDIE)

Defining learning objectives, role-based syllabus, case studies from your processes, and the governance map (NIST/ISO 42001/OWASP/UU PDP).

Before delivery

Delivery — Wave 1 (Champions)

A champion group is trained first (70-20-10 pattern) to drive adoption and validate the material before scaling.

First wave

Delivery — Subsequent Waves

Next batches roll out across departments with per-function case studies and practice labs.

Rolling per batch

Kirkpatrick Evaluation

Level 1-4 measurement (reaction, learning, behavior/adoption, results). Phillips ROI Level 5 if finance requests a monetized figure.

After each wave

Follow-Up & Institutionalization

Office hours, monthly adoption reviews, a 30-60-90 day plan, and an organizational AI maturity roadmap.

Ongoing

Case Studies

Typical Outcome Patterns

Indicative impact patterns based on similar program structures — illustrative, with no named clients or promised numbers.

Marketing & communications team at a financial-services institution

Intervention

2-day bootcamp + prompt library + customer-data guardrail SOP

Result

Shorter campaign-content production cycle and more consistent output; AI usage follows the data rules agreed with legal

Multi-plant manufacturing operations division

Intervention

Multi-month ongoing program + champion network (70-20-10)

Result

Several standardized report-automation use cases across plants, with human validation before use

Secretariat & policy unit of an agency/state-owned enterprise

Intervention

Guided workshop + UU PDP-aligned AI usage policy

Result

Faster document drafting with an auditable usage trail

Procurement Info

Information for Procurement & Vendor Management

What procurement, finance, legal, and information-security teams need.

Legal entity

Registered PT under the Selestia ecosystem (Eduprima group); complete tax ID & legal documents; ready for service agreements and vendor onboarding.

Proposal

Structured proposal: measurable learning objectives, syllabus, governance map (NIST/ISO 42001/OWASP/UU PDP), facilitator profiles, schedule, and TNA-based cost breakdown.

Pricing model

TNA-based — flat per program, per session, per participant, tiered, or custom. No standard figure without a needs analysis; an estimate follows the TNA.

Payment & tax

Flexible terms (deposit + balance / per-batch terms); tax invoice (PPN/VAT) and PO documentation support available.

BUMN/government process

Familiar with state-owned-enterprise/agency procurement stages: vendor documents, e-procurement, owner's estimate/bid, and compliance clauses.

Measurement

Kirkpatrick Level 1-3 evaluation report (attendance, AI policy assessment, exercise results); Phillips ROI Level 5 on finance's request.

Confidentiality & data security

NDA signing, participant data confidentiality clauses, and practice that does not force confidential data into public AI (aligned to OWASP LLM & UU PDP).

Material ownership

The prompt library & SOPs built for the company belong to the company; training-material usage rights are agreed in the contract.

FAQ

Frequently Asked Questions

Next Step

Discuss your team's AI training needs

Start with a free training needs analysis: we map your roles, processes, and AI maturity, then build a proposal & budget estimate grounded in real needs.

Complimentary training needs analysis — the natural first step
Proposal, syllabus, and governance map within a few business days
Procurement-ready documents (company profile, tax ID, NDA, VAT invoice)
Kirkpatrick impact measurement (Phillips ROI on request)

Request Proposal & Free TNA Free Consultation

Explore More

Discuss your team's AI training needs

Start with a free training needs analysis: we map your roles, processes, and AI maturity, then build a proposal & budget estimate grounded in real needs.

Complimentary training needs analysis — the natural first step
Proposal, syllabus, and governance map within a few business days
Procurement-ready documents (company profile, tax ID, NDA, VAT invoice)
Kirkpatrick impact measurement (Phillips ROI on request)

Corporate Generative AI Training

Corporate Generative AI Training

Expected Outcomes

Program Format Options

Guided Use-Case Workshop (1 day)

Fundamentals + Role-Based Bootcamp (2-3 days)

Agentic & RAG Intensive (3-5 days)

Ongoing Program (3-6 months)

Discuss your team's AI training needs

Curriculum Framework

1Generative AI Foundations

2Applied Prompt Engineering

3AI for Real Work (by role)

4Automation & Agentic Workflows

5AI Governance, Risk & Security

6Institutionalization & Impact Measurement

Choosing a Program Format

Who Is This Program For?

Knowledge workers (marketing, operations, finance)

Managers & team leads

HR, L&D & People Development teams

IT, Data, Security & Legal/Compliance teams

Use Cases by Industry

Delivery

In-house on-site

Live online

Hybrid

Engagement Path

Training Needs Analysis (TNA)

Program Design (ADDIE)

Delivery — Wave 1 (Champions)

Delivery — Subsequent Waves

Kirkpatrick Evaluation

Follow-Up & Institutionalization

Typical Outcome Patterns

Information for Procurement & Vendor Management

Frequently Asked Questions

Is the training tailored to our business processes?

How is data security handled when employees use AI?

Is this training aligned with recognized AI governance frameworks?

How is the ROI or impact of the training measured?

What tooling and models are used during training?

Is in-house or public-class training better?

What is the ideal batch size?

How far can the program be customized per department?

Is there post-training support?

Is there an evaluation report and certificate?

Discuss your team's AI training needs

Related Topics

Discuss your team's AI training needs