NTUC-KPMG AI workforce programme: prepare the employer baseline

The NTUC-KPMG AI workforce transformation programme announced on 22 October 2026 connects AI adoption with workforce planning, job redesign, training and technology. Its public announcement says companies can tap the NTUC Company Training Committee Grant and the Enterprise Workforce Transformation Package at different stages. An employer should still arrive with a clear business problem, current workflow and worker outcomes, because participation does not itself prove grant eligibility or project value.

Define the operating problem

Choose one workflow where delay, error, capacity or customer experience can be measured. Record current volume, cycle time, rework, cost, service levels and exceptions. Avoid beginning with a preferred AI product and searching for a problem it can claim to solve.

Name the executive sponsor and process owner. Explain why the problem matters now and what would happen without intervention. This baseline makes it possible to distinguish technology impact from normal business fluctuation.

Map work at task level

Break the workflow into tasks, decisions, handoffs and controls. Record who performs each step, what information is used and where judgement or customer interaction matters. AI may remove, change or create tasks rather than replacing an entire job.

Include informal work such as checking, chasing, correcting and explaining. These activities often contain the real burden and risk. Validate the map with employees who perform the work, not only managers or the technology supplier.

Identify affected roles and skills

List each role, employee group, location and employment arrangement affected by the proposed change. Describe current skills and the future tasks expected. Separate product training from durable capabilities such as data judgement, exception handling, customer communication and process improvement.

Record skills gaps with evidence from work samples, supervisor review or structured assessment. Do not label staff resistant or unready merely because they raise control, workload or job-security concerns.

Set worker and business outcomes

Define business outcomes and worker outcomes together. Business measures may include throughput, quality, cost or service. Worker measures may include redesigned responsibilities, skill attainment, progression, wages or reduced low-value work. State the baseline, target, owner and measurement period.

The NTUC announcement describes better jobs and business value, but it does not guarantee a result for every employer. Use conservative assumptions and retain the original forecast for later comparison.

Choose the support route after diagnosis

Map the proposed activity to the current CTC Grant, WDG(JR+), redesigned SFEC or an unfunded route. Each has its own applicant, cost, programme and evidence conditions. A partnership announcement is not a Letter of Offer and does not combine the schemes into one entitlement.

Use the EWTP programme-partner preparation guide for the broader route. Ask which organisation administers each stage, who submits and what cannot begin before approval.

Plan employee consultation

Explain the problem, proposed trial, affected tasks, data use, training and decision process to employees early enough for input to change the design. Record questions and responses. Consultation is more useful when staff can identify exceptions, unsafe shortcuts and customer needs.

Set escalation and feedback routes during implementation. Protect staff from being evaluated on unstable AI output or an untested process. Where roles or employment terms may change, obtain appropriate HR and legal advice.

Control the AI component

Document data access, vendor terms, security, model limitations, human review and fallback. Define which decisions the tool may support and which remain with an authorised employee. Test difficult and sensitive cases before broader use.

Connect the control record to the AI production budget guide. Workforce funding does not remove the employer’s duty to manage technology, privacy, security and customer risk.

Prepare an evidence-led intake pack

Bring the workflow map, baseline measures, role inventory, skills gaps, proposed outcomes, management sponsor, employee-consultation plan, technology assumptions and current support-route questions. Mark facts, estimates and unresolved items separately.

After onboarding, record advice, programme fit and next actions. Verify any funding pathway on the controlling official page and preserve the application receipt. The enterprise support and workforce hub connects related redesign and training guides.

Design a bounded first workflow

Choose a workflow with enough volume to measure but a manageable impact if the trial fails. Define the start and end of the process, employee groups, customer touchpoints and systems. Exclude adjacent work deliberately. A bounded first project makes training, consultation and benefit measurement more credible than a promise to transform the whole company.

Prepare a before-and-after task table. For each task, state whether it will remain, be assisted, be automated, be removed or be newly created. Add the future skill and control owner. This table helps employees see that AI adoption changes the design of work, and it gives management a way to test whether low-value effort actually falls rather than moving to another team.

Set review dates at trial midpoint, launch and ninety days after operation. Measure error, rework, service, employee workload and adoption alongside speed or cost. Decide in advance what evidence supports expansion, redesign or withdrawal. Funding and course participation are inputs; the operating and worker outcomes determine whether the project should continue.

Keep the approved baseline with the later results and employee feedback. If benefits fall short, identify whether the cause was technology, data, workflow design, training or adoption. This prevents a weak outcome from being explained only as staff resistance and gives the next decision a fair evidence base.

The useful first deliverable for the NTUC-KPMG AI workforce programme is an honest employer baseline. It gives advisers and workers a shared operating problem, makes funding routes easier to test and creates a fair measure of whether transformation improves both work and business performance.