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Digital & AI Upskilling

Generative AI Training

Equip your teams to use generative AI on real work: summarising long documents, drafting, reviewing data, and designing workflows that lean on AI. The ground rules are set from the start so company data and personal data stay safe under Indonesia PDP Law obligations.

  • 4.9/5 · 287 reviews
  • 300+ clients nationwide
  • 2,000+ trainers
  • 1 to hundreds of participants
  • Standard · Premium · Private 1-on-1
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format
In-house / online / hybrid
duration
3 days per cohort
participants
8-30 per batch
language
Indonesian / English

2,000+ trainers and experts

Our group's teaching network, matched to what your team needs.

See class documentation

You see your trainer first

The trainer's profile, track record and field come with the proposal, before you decide.

4.9 out of 5

287 participant reviews.

300+ clients nationwide and 180,000+ private training sessions completed. Among them: MPR RI, Akademi Militer Magelang, DPRD Provinsi Jawa Timur.

One person to hundreds

Private coaching for one participant, a class for one team, or several cohorts at once.

Three budget levels

  • Standard: core class, material fitted to the participants’ work
  • Premium: senior trainer, deeper material design
  • Private 1-on-1: one participant, one trainer, schedule set by the participant
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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.

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.

Clients & Documentation

Some of the institutions we have trained

Photos from sessions we have delivered, each one named after the institution it belongs to.

See class documentation (123 photos)

Generative AI in Everyday Work

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 instead of 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
7Governance references we work from: NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, and Indonesia PDP Law obligations.

Measurable Outcomes

Expected Outcomes

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

AI tools actually get used
Most participants use AI in their weekly workflow within 30 days post-training. Read from the usage logs of AI tools in the work environment (Kirkpatrick L3).
Document work finishes faster
Reduced time on drafting, summarization, and repetitive analysis, measured against the team baseline. Visible in shrinking turnaround on drafts and summaries (Kirkpatrick L4).
The AI usage rules are followed
All participants pass the AI policy & data guardrail assessment (aligned to OWASP LLM & UU PDP). Judged through an AI usage policy quiz closing the class (Kirkpatrick L2).
Internal use cases start running
At least 2 production-ready use cases per department, documented with feasibility criteria. Proven by internal use cases running after the class (Kirkpatrick L4).
A prompt library and SOPs owned by the company
A validated prompt library plus AI usage SOPs approved by legal/security. What you hold is a shared prompt collection the team maintains itself (Kirkpatrick L3 transfer).
Monetary value of the time saved (optional)
Net-benefit calculation with isolation of training effects, when finance requires a figure. Time saved converts to rupiah through Phillips L5 by request.

Program Format

Program Format Options

Chosen by AI maturity, population size, and operational schedule, finalized after the TNA. A single cohort can already work with AI productively and safely after those three days. Companies wanting AI usage spread evenly across every division attach an ongoing three to six month programme with material written per role.

1

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
2

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
3

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
4

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

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.

  • How LLMs work: tokenization and context window, no jargon
  • Capabilities vs limits: hallucination, knowledge cutoff, non-determinism
  • Public vs enterprise LLMs (ChatGPT Enterprise, Copilot, Gemini, internal models)
  • Enterprise tooling landscape & model selection criteria
  • Ethics basics & risk framing: introduction to NIST AI RMF

  • Prompt anatomy: role, context, instruction, output format, examples
  • Reliable patterns: zero-shot, few-shot, chain-of-thought, ReAct
  • Decomposition, self-critique, and hallucination-reduction techniques
  • Grounding output to trusted sources (introduction to RAG)
  • Building & validating a prompt library per business function
  • Output quality evaluation: rubrics, golden sets, and prompt regression testing

  • Document drafting & summarization: memos, reports, minutes, correspondence
  • Data & spreadsheet analysis: interpretation, formulas, findings narrative
  • Structured research & QA with source verification
  • Use cases for marketing, operations, finance, HR, legal, and IT
  • Use-case feasibility criteria & how to assess early ROI

  • Retrieval-augmented generation (RAG) over internal documents
  • Internal assistants: architecture patterns & data boundaries
  • Agentic workflows: tool use, orchestration, human-in-the-loop
  • Excessive agency & system prompt leakage (OWASP LLM06 & system prompt)
  • Safe automation boundaries: when AI must not decide alone

  • NIST AI RMF: Govern, Map, Measure, Manage functions applied to teams
  • OWASP LLM Top 10 2025: prompt injection, sensitive information disclosure, improper output handling, data poisoning
  • ISO/IEC 42001: the role of an AI Management System & AI impact assessment
  • UU PDP No. 27/2022: controller/processor duties, the DPO role, data that must never enter public AI
  • Drafting the company AI usage policy & SOP + acceptable use policy
  • Bias, fairness, transparency, and an AI usage audit trail

  • Building an AI Center of Enablement & a champion network (70-20-10)
  • Kirkpatrick Level 1-4 evaluation for an AI program
  • Phillips ROI Level 5: monetization & isolating training effects
  • 30-60-90 day adoption plan & office-hours cadence
  • Organizational AI maturity roadmap after the program

Comparison

Choosing a Program Format

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

AspectGuided Workshop (1 day)Fundamentals Bootcamp (2-3 days)Agentic & RAG Intensive (3-5 days)Ongoing Program (3-6 mo)
Primary goalQuick win on 1-2 use casesBroad literacy & adoptionDeep technical capabilityTransformation & measured impact
Ideal participants1 team with a concrete caseMany cross-functional teamsIT/data/product teamsLarge population, phased
Governance depthBasic use-case guardrailsAI policy module + UU PDPOWASP LLM + agentic safetyFull SOP + institutionalization
Evaluation levelKirkpatrick L1-L2Kirkpatrick L1-L3Kirkpatrick L1-L3Kirkpatrick L1-L4 (+Phillips L5)
Best suited forValidate fast before scalingInitial organizational roll-outBuild internal AI solutionsOrg-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.

See in Banking & Financial Services context →
Technology & Startups

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

See in Technology & Startups context →
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.

See in State-Owned Enterprises (BUMN) context →
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).

See in Manufacturing context →
Retail & FMCG

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

See in Retail & FMCG context →
Government & Public Sector

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

See in Government & Public Sector context →

Delivery Method

Delivery

Format adapts to team distribution and operational schedule; every format is hands-on practice.

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.

1

Training Needs Analysis (TNA)

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

Initial stage
2

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
3

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
4

Delivery: Subsequent Waves

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

Rolling per batch
5

Kirkpatrick Evaluation

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

After each wave
6

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.

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

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 throughout, replacing generic examples. Instructional design follows the ADDIE approach so learning objectives are measurable and accountable.

The governance module is core to the program, treated as a foundation. 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.

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 to prepare your teams and SOPs to align with these frameworks without claiming a certification.

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.

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.

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.

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.

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, well beyond a uniform template.

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.

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. When HR requests it, staff who finish the AI-policy exercises are issued a participation certificate. This completes the procurement requirement for proof of delivery.

Participant count and the number of cohorts form the first calculation, followed by participant seniority, since classes for general staff, for technical teams, and for the leadership are written at different depth. Material built specifically per division adds case preparation work. The number of meetings, whether the class sits at the office or runs remotely, and the host city all count. When exercises use your internal documents, preparing and masking that data counts too. We build the figure once your training needs analysis stage is done.

The class sits well between 8 and 30 participants, and twenty-odd staff finish inside one cohort within three days. What genuinely needs arranging beforehand is access to the AI tools they will use, so participants practice in the same environment as their actual work. After the class, the new habit usually settles within weeks as long as a shared prompt collection exists and someone is named to maintain it.

The way out is changing the trainer. AI material easily drifts too technical or too shallow depending on who carries it. Notify the programme coordinator, and a replacement validated applied AI trainer takes the class on. Teaching material, the prompt collection built in class, and participant progress notes are recorded in our operations system, and the next trainer picks up the last section.

Next Step

Discuss your team's AI training needs

We map your roles, processes, and AI maturity, then build a proposal & budget estimate grounded in real needs.

  • Proposal, syllabus, and governance map
  • Procurement-ready documents (company profile, tax ID, NDA, VAT invoice)
  • Kirkpatrick impact measurement (Phillips ROI on request)
Request a ProposalFree Consultation

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Discuss your team's AI training needs

We map your roles, processes, and AI maturity, then build a proposal & budget estimate grounded in real needs.

  • Proposal, syllabus, and governance map
  • Procurement-ready documents (company profile, tax ID, NDA, VAT invoice)
  • Kirkpatrick impact measurement (Phillips ROI on request)

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