Collecting generative AI use-case ideas is easy; turning one into an operating decision is not. At a Thailand or ASEAN site, headquarters, local management, users, IT, security and legal teams often begin with different assumptions, while Thai, Japanese and English coexist in the same workflow. This guide shows how to discover real workflow candidates, score value, feasibility, risk and data readiness, and carry the winner into a 90-day proof of concept, comparable RFP and evidence-based acceptance decision. The deliverable is not an inspiration list. It is evidence for deciding what to build, what to buy and what not to automate.
The answer: filter generative AI ideas through workflow, evidence and gates
Use this sequence:
- Collect recurring workflows with observable friction from every department.
- Map the input, decisions, output, downstream user and accountable owner.
- Apply disqualifiers first; score only the candidates that pass.
- Test the top one or two with real data, users and failure conditions for 90 days.
- Put the same scenarios into the RFP and decide Do/Buy using TCO and operating responsibility.
- Accept on business KPI, quality, risk, adoption and recovery—not accuracy alone.
OpenAI says it analysed more than 600 customer use cases and found that most fit six recurring primitives. It then recommends an impact/effort framework and moving from isolated tasks toward department workflow mapping. The practical lesson is that the number of ideas matters less than a common evidence system for comparing them.
Why a “100 generative AI examples” list cannot make the decision
External AI implementation examples are useful prompts, but they do not prove your data quality, approval path, languages, contracts, systems or available owners. Meeting summarisation may work elsewhere yet be unsuitable when your meetings mix employee evaluation and customer-confidential content. Maintenance-report drafting may become faster, but inconsistent equipment codes and failure categories can make downstream analysis worse.
OpenAI Academy’s workflow-readiness resource, updated in September 2026, starts from an observable workflow problem and examines frequency, reach, friction, repeatability and business relevance. It separates known, inferred and unknown information and routes an idea to one of four outcomes: test now, validate further, sequence later or avoid for now. It explicitly warns against assuming that every workflow problem needs AI.
If a purchase approval waits three days because the approver is absent, text generation is not the root solution; delegation and workflow notification come first. If staff repeatedly interpret Thai requests, extract item, date and evidence, then draft a Japanese response, an AI-assisted workflow with human approval may be valuable.
A five-stage workshop for AI workflow opportunities
Do not rely on one 90-minute brainstorm. Combine observation, department workshops and cross-functional consolidation. Invite downstream recipients and approvers as well as the people performing the task.
1. Record an event, not a department
“Use AI in quality” is too broad. Record who receives what, how often; what they read, compare and decide; the output format and language; who checks and records it; and where delay, rework or inconsistency occurs. A useful name is “classify Thai customer enquiries, create a Japanese first-response draft with source links, and register it in CRM.” That boundary makes inputs, expected outputs, reviewers and error impact testable.
2. Use six primitives to avoid blind spots
| Primitive | Thailand/ASEAN candidate | Human judgment retained |
|---|---|---|
| Create and transform | Multilingual report drafts and simplified work instructions | Release approval and technical accuracy |
| Summarise and extract | Extract actions from email, meetings and audit findings | Priority, commitment and exceptions |
| Search and synthesise | Answer from policies, specifications and past incidents with citations | Source validity and latest-version check |
| Analyse and explain | Explain patterns in defect comments and downtime reasons | Causality and action approval |
| Software support | Draft SQL, macros and test cases | Execution permission and code review |
| Automation and agents | Move from routine enquiry to draft and record creation | Approval for sending, ordering and updating |
These primitives generate candidates; they are not product features. Visual inspection, demand forecasting and finite-capacity scheduling are primarily computer-vision, predictive or optimisation problems. Generative AI may explain results or draft an exception ticket, but the core model requires different evaluation.
3. Measure a baseline
Replace “this takes too long” with volume, handling time, waiting time, rework, error type and operator variation. For daily work, observe at least one representative week; for monthly work, use the latest three to six cycles. Record confidentiality, personal data and contractual transfer limits at the same time.
Look beyond averages. A workflow that usually takes ten minutes but takes ninety for exceptions may create more value through early exception detection than a lower average. In multilingual work, record source language, output language, available reviewers and translation handoffs.
4. Map one step upstream and downstream
A fast draft has little value if staff must re-enter a missing customer code. Automatic ERP registration increases value but also increases the consequences of error and requires reversal. The map must show the producer of the input, the AI-assisted step, downstream users, systems of record and exception owner.
5. Create a one-page use-case card
| Field | Content |
|---|---|
| Workflow problem | One observable sentence |
| Users and owners | Actual users, process owner, approver |
| Frequency and volume | Daily cases, peaks, sites |
| Input and output | Format, language, classification, gold examples |
| Baseline | Time, quality, waiting, cost and risk |
| AI role | Draft, extract, search, recommend or execute |
| Human intervention | Review, reject, correct and escalate |
| Dependencies | APIs, master data, permissions, policies, downstream steps |
| Unknowns | Questions the PoC must answer |
Twelve candidate generative AI use cases for ASEAN operations
These are workshop starters, not recommendations.
| Function | Candidate workflow | First value hypothesis | Main caution |
|---|---|---|---|
| Sales | Classify multilingual enquiries and draft replies | Response time and completeness | Wrong commitments, pricing, confidentiality |
| Sales engineering | Extract compliant and unresolved points from specifications | Review effort | Assertions, version and units |
| Procurement | Compare quotation terms and draft clarification questions | Comparison time and omitted terms | Currency, tax, Incoterms, final selection |
| Planning | Explain schedule-change reasons for daily reports | Reporting and handover | System-of-record quantities, false causality |
| Quality | Classify issue narratives and support an 8D draft | Preparation time and consistency | Root-cause assertion and customer approval |
| Maintenance | Search history and propose inspection points | Search time and tacit knowledge | Safety, machine conditions and work permits |
| Warehouse | Explain inventory exceptions across languages | Handoffs and training | Lot, quantity and mis-shipment |
| EHS | Create policy-grounded training and quizzes | Material preparation | Current law and permit-to-work |
| HR | Policy Q&A and application guidance | Enquiry handling | Personal data and labour decisions |
| Finance | Organise evidence explanations and reconciliation differences | Month-end preparation | Posting approval and tax advice |
| IT | Triage tickets, search runbooks and draft code | First resolution and build time | Privilege, vulnerability and production execution |
| Management | Synthesise issues across site reports | Meeting preparation | Missing data and selective summaries |
An early PoC is often easier when AI reads, searches or drafts while a person decides. That is not a universal rule: a monthly low-risk task may rank below a daily medium-risk workflow with strong controls.
The ILO’s 2026 ASEAN brief estimates, using 2025 data, that 22.9% of ASEAN employment—nearly 80 million workers—has more than minimal potential exposure to generative AI. The highest-exposure category is 3.3%, or 11.7 million workers; Thailand is estimated at 20.6% with more than minimal exposure. Exposure is not headcount reduction, adoption or productivity. Use it to decompose tasks and decide where AI assists and where judgment stays with people.
Put disqualifiers before scores
Do not let high benefits mathematically cancel a severe risk. Stop or redesign if:
- the workflow uses personal data without confirmed purpose and authority;
- a customer or headquarters contract prohibits external AI processing;
- AI would make an unattended final decision on life, safety, employment, payment or shipment quality;
- no accountable expert can verify the answer and evidence;
- ownership, deletion and retention cannot be agreed;
- users cannot return safely to the original procedure; or
- the supplier cannot explain treatment of inputs, outputs and logs.
ASEAN’s Expanded Guide highlights six GenAI risks: mistakes and anthropomorphism, factually inaccurate responses and disinformation, deepfakes/impersonation/fraud, IP infringement, privacy/confidentiality and embedded bias. It organises recommendations across nine dimensions including accountability, data, trusted deployment, incident reporting, testing and assurance, and security. NIST’s GenAI Profile describes 12 risk areas and more than 200 suggested actions. These belong at the selection gate, not as a late legal checklist.
This article is not legal advice. Confirm Thailand PDPA, cross-border transfer, labour, sector and customer-contract obligations against the actual data and use case. ETDA’s organisational GenAI governance guideline is a useful Thailand-specific reference for roles and controls.
A proposed 100-point scorecard
The following is a consulting proposal, not an official threshold. Calibrate weights to business priorities and risk tolerance and retain evidence for every score.
| Dimension | Proposed weight | Questions | Evidence |
|---|---|---|---|
| Value | 35 | Frequency, reach, time, waiting, quality, revenue/cost link | Logs, samples, KPI baseline |
| Feasibility | 25 | Process stability, AI fit, integration, owner, change effort | Workflow, API, technical spike |
| Risk control | 20 | Error impact, personal/confidential data, bias, IP, safety, oversight | Risk register, contract, tests, approvals |
| Data readiness | 20 | Quantity, representativeness, quality, rights, freshness and gold answers | Data inventory, missing rate, evaluation set |
| Total | 100 | Same definitions across candidates | Evidence link per score |
Score each sub-item from zero to five: 0 unknown, 1 hypothesis, 2 small sample, 3 owner-confirmed, 4 measured, 5 reproduced under multiple conditions. Do not turn “unknown” into a neutral midpoint; route it to validation.
| Disposition | Meaning | Next action |
|---|---|---|
| Test now | Credible value, owner, users, data and gates are present | Limited workflow PoC |
| Validate further | Value looks plausible, but time, gold answers or data rights are unknown | One-to-two-week measurement or data diagnosis |
| Sequence later | APIs, master data, policy or standard work must come first | Complete dependency project |
| Avoid for now | Low value, poor AI fit or uncontrolled severe risk | Non-AI improvement or stop |
A company might propose 70/100 for PoC and 55–69 for validation, but these are only sample thresholds. More important is a minimum for each critical dimension. A safety workflow might require at least 4/5 for risk control; internal drafting might use 3/5 with mandatory human review.
Illustrative comparison
| Candidate | Value 35 | Feasibility 25 | Risk 20 | Data 20 | Total | Result |
|---|---|---|---|---|---|---|
| Multilingual enquiry reply draft | 28 | 20 | 15 | 15 | 78 | Test now |
| Quality 8D draft | 24 | 16 | 12 | 14 | 66 | Validate further |
| Automatic maintenance instruction | 30 | 12 | 6 | 11 | 59 | Avoid for now |
| Monthly report issue synthesis | 20 | 21 | 17 | 16 | 74 | Test now |
This is fictional. The maintenance idea does not proceed despite high value because safety and erroneous action are insufficiently controlled. The reply draft can proceed in a limited test because a person checks it before sending and sources can be restricted to approved material.
Turn the AI PoC from a demo into a 90-day decision
Ninety days is a proposed decision window, not a promise of production rollout.
| Period | Purpose | Deliverables | Gate |
|---|---|---|---|
| Days 0–30 | Fix problem, baseline, data and risk | Workflow, evaluation set, KPI, RACI, risk register | Stop without gold answers and owner |
| Days 31–60 | Limited use with real users | Prototype, logs, changes and training | Meet quality floor and business KPI? |
| Days 61–90 | Test exceptions, attacks, outages and operations | Acceptance result, TCO, RFP response, run/exit plan | Approve Go / Revise / Stop |
Days 0–30: build the answers first
Create an evaluation set covering routine cases, exceptions, missing data, Thai spelling variation, mixed languages, old versions and prohibited inputs. A proposed starting point for a daily document workflow is 100 cases—60 routine, 25 difficult and 15 prohibited/adversarial. This is not a universal statistical guarantee; increase it with volume and error severity.
Every case needs an expected result, tolerance, evidence, reviewer and severity. Measure required-field extraction, source agreement, unsupported assertions, prohibited disclosure and review time—not only fluent writing.
Days 31–60: measure workflow friction
Include experienced and new users, Thai-first users and downstream reviewers, not only champions. Compare completion time, editing, return, refusal, questions and waiting. Version the model, prompt, retrieval scope and permissions when anything changes.
Days 61–90: test failure and recovery
Use wrong and outdated sources, inaccessible documents, ambiguous requests, prompt injection, API outage, timeout, duplicate registration and user over-trust. Practise who stops the service, isolates an incident, restores the old process and reports it. Apply NIST’s TEVV concept to the complete workflow context, not only the model.
Write an RFP around scenarios, not adjectives
“Latest model” and “high accuracy” are not comparable requirements. Give every bidder the same inputs, expected outputs and failure conditions.
| Requirement | RFP question | Acceptance evidence |
|---|---|---|
| Workflow | In/out scope, users, upstream/downstream | Agreed workflow map |
| Quality | Metric, evaluation set, language results, retest | Case results and error log |
| Data | Retention, training use, region, encryption, deletion, transfer | Contract, architecture and deletion evidence |
| Security | SSO, roles, audit, secrets and attack controls | Role tests, logs and response process |
| Human control | Review, reject, correct and execution rights | Screen and approval log |
| Integration | Limits, retries, idempotency, monitoring and outage | Failure test and reconciliation |
| Operations | Change, reevaluation, incident, SLA and training | RACI, runbook and drill |
| Commercial | Initial/recurring cost, usage, language/site growth and exit | Three-year TCO and exit terms |
NIST discusses third-party due diligence for data, IP, privacy and security, and transparency through instruments such as SLAs. ISO/IEC 42001 specifies requirements to establish, implement, maintain and continually improve an AI management system. Certification need not be mandatory for every procurement, but the management-system lens helps suppliers explain accountability, measurement and change after launch.
Do or Buy: decide from the operating boundary
Do/Buy is a spectrum: standard service, configurable SaaS, external build, co-development and in-house delivery.
| Axis | Buy-leaning | Do-leaning |
|---|---|---|
| Differentiation | Standard workflow | Proprietary know-how is strategic |
| Data/integration | Common documents and connectors | Unique equipment, roles and databases |
| Change | Accept supplier release timing | Control model, evaluation and release timing |
| Capability | Operations owner, limited developers | AI, data, security and SRE capability |
| Contract | Standard terms satisfy needs | Special residency, audit or IP conditions |
| Exit | Data export enables migration | Logic and evaluation assets must be owned |
A practical hybrid is “buy the foundation, do the workflow and evaluation set.” Procure model and identity services while retaining prompts, sources, approvals, KPI and evaluation data as company assets. For a small number of users doing standard summaries, an in-house platform may cost more to operate than it returns.
Compare three-year TCO on consistent assumptions: licences/APIs, data preparation, integration, evaluation, security review, translation, training, monitoring, retesting after model changes, incidents and exit. This article invents no prices because usage and contracts differ.
Acceptance: accuracy is not enough for Go
Sign the criteria before the PoC. Proposed examples include:
- median handling time at least 20% below baseline;
- zero critical errors and at least 95% match on required fields;
- at least 95% source agreement for citation-required cases;
- return rate no worse than baseline and 80% of users willing to continue limited use;
- no prohibited disclosure or retrieval outside permission;
- restoration of the original process within 30 minutes; and
- measured benefit per case expected to exceed full operating cost per case.
These are proposals, not universal standards. Quality, safety, HR and finance need larger evidence sets, expert review and possibly dual approval. Low-risk internal drafting can use different thresholds with mandatory human correction.
Use four outcomes: Go, Conditional Go, Redesign and Stop. “Good Japanese but broken Thai names,” “accuracy passed but review time increased,” and “normal cases pass but source updates fail” should not be forced into a binary answer.
After the PoC: treat model change as workflow change
Inputs, policies, organisations, models, prompts and source documents change. Review usage, time, correction, critical error, unanswered cases and complaints monthly; rerun the evaluation set quarterly and after material changes. Define stop authority and rollback of model, prompt and data.
OpenAI’s 2026 enterprise interviews repeatedly point to early partnership with security, legal, compliance and IT, quality definitions before scale, workflow ownership and hybrid human oversight. The process owner—not only the AI team—must own outcomes and quality.
At minimum assign an executive sponsor, process owner, local user representative, IT/data, security, legal/privacy and supplier. In an enquiry workflow, sales owns the outcome; IT supports the system; legal defines the permissible boundary.
Common failures and corrections
- Popularity vote: attractive demos win. Require evidence cards and common scorers.
- Enterprise chatbot first: scope, owners and gold answers are too broad. Start with one department and a bounded corpus. See our generative AI implementation roadmap and cost guide.
- Accuracy-only PoC: measure end-to-end handling, waiting, correction and returns. See AI adoption effect measurement and 30/60/90-day decisions.
- Vendor demo called a PoC: use company exceptions, permissions, languages and outage tests.
- Choosing one vendor before the problem: discovery, governance, implementation and adoption are different capabilities. See four advisor types for AI adoption.
FAQ about use cases, business adoption and AI PoCs
How many generative AI ideas are enough?
Quality and cross-functional coverage matter more than count. A proposed approach is five to ten cards per department, consolidated into twenty to forty candidates for first scoring. Adjust to organisation size.
Can we copy another company’s AI implementation case?
Use it for inspiration, not evidence. Remeasure frequency, data, gold answers, permissions, downstream effects and risk in your company.
What is a good first workflow?
A frequent workflow with clear input and output, human verification and reversible errors. Multilingual summarisation, classification, grounded search and standard document drafts are candidates, not automatic winners.
What should we check in an AI case study?
Population, period, baseline, users, workflow boundary, method and exclusions. “Productivity increased” is incomplete without what was measured and how many cases.
Does an AI PoC finish in 90 days?
Ninety days here is a proposed decision window. Regulation, high risk, large integration and data work may require longer. It does not promise complete rollout, training and maintenance.
What accuracy percentage is enough?
There is no universal number. Use error severity, human review, volume, evidence and recoverability, and track critical errors separately from averages.
When should Do/Buy be decided?
After the workflow, data boundary and acceptance scenarios are clear. Choosing a product first biases discovery toward problems that product can show.
Conclusion: turn an idea list into a decision asset
Start generative AI use-case discovery from observable workflows. Apply disqualifiers, then compare value, feasibility, risk control and data readiness. Test the winner through normal, exception, adversarial and outage conditions in a 90-day decision cycle. Carry the same scenarios into the RFP and acceptance plan, then decide Do/Buy through data, change ownership, TCO and exit—not model appeal.
TOMAS TECH supports workflow inventory, use-case scoring, data diagnosis, a 90-day PoC, RFP and acceptance criteria, and integration for Thailand and ASEAN operations—even before a product is selected. If you have too many candidates and no defensible way to choose one, contact us.
References
- OpenAI, “Identifying and scaling AI use cases,” https://openai.com/business/guides-and-resources/identifying-and-scaling-ai-use-cases/ (accessed 7 September 2026)
- OpenAI Academy, “Evaluate AI workflow readiness,” https://academy.openai.com/en/public/clubs/champions-ecqup/resources/ai-use-case-discovery-and-prioritizer-2026-05-07 (accessed 7 September 2026)
- OpenAI, “How enterprises are scaling AI,” https://openai.com/business/guides-and-resources/how-enterprises-are-scaling-ai/ (accessed 7 September 2026)
- NIST, “AI Risk Management Framework,” https://airc.nist.gov/airmf-resources/airmf/ (accessed 7 September 2026)
- NIST, “AI RMF: Generative Artificial Intelligence Profile,” https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf (accessed 7 September 2026)
- ASEAN Secretariat, “Expanded ASEAN Guide on AI Governance and Ethics – Generative AI,” https://asean.org/wp-content/uploads/2025/01/Expanded-ASEAN-Guide-on-AI-Governance-and-Ethics-Generative-AI.pdf (accessed 7 September 2026)
- ETDA, “Generative AI Governance Guideline for Organizations,” https://www.etda.or.th/getattachment/6050a4b7-defd-4dba-8cbc-ff6a444a3d08/20240910_GenerativeAIGovernanceGuideline_Vol1_AIGC.pdf.aspx (accessed 7 September 2026)
- ISO, “ISO/IEC 42001:2023 — AI management systems,” https://www.iso.org/standard/42001 (accessed 7 September 2026)
- ILO, “Generative AI and labour markets in ASEAN,” https://www.ilo.org/publications/generative-ai-and-labour-markets-asean-significant-exposure-limited (accessed 7 September 2026)
The weights, thresholds, case counts, 30/60/90-day stages and acceptance values in this guide are consulting proposals. Confirm current law, contracts, prices and service specifications for the actual country and use case before a decision.
