Who it is for
Teams trained by us or elsewhere, or already experimenting with AI, with a task repeated several times in 30 days, such as weekly reporting or meeting preparation, so there is time for more than one trial and revision.
AI ADOPTION & GOVERNANCE
Start AI where it matters, while keeping control of critical decisions
Assess readiness, design a pilot and plan team adoption around business priorities, data and acceptable risk.
Connect purpose, people, processes and technology
We assemble a collaborative team around the challenge and expertise needed. Each proposal identifies facilitators, roles, duration and scope before agreement.
Choose workshops, process improvement or AI adoption around the problem. Consulting is scoped and quoted separately from workshop packages.
Our team and approach →Useful AI does more than produce answers. It respects data boundaries, has a clear owner and delivers outcomes that can be verified.
DATA READINESSPDPA-AWAREHUMAN REVIEWVALUE CAPTURE
AI IN 30 DAYS
ONE TEAM · ONE WORKFLOW · FOUR WEEKLY REVIEWS
Put AI to work with your team in 30 days
Move from learning or experimenting with AI to applying it to one team workflow. Four weekly reviews build before-and-after evidence to decide whether to continue, adjust or stop.
What to prepare
One team, one workflow, a named owner, existing work samples and time to practise together. Confirm approved data and tools before starting. A task performed only monthly may need a longer observation period.
Scope and pricing
Scoped and quoted before starting, separately from workshop fees. The proposal confirms participants, session duration, support hours and revision rounds. The 30 days are an observation and support window, not a full-time adviser.
The four-week plan
Choose the task and baseline
Confirm the owner, current steps, time, quality and success criteria. Choose one task that can be tested repeatedly.
Design and try it
Set up the workflow, tools and templates. Test with approved data and define human review points.
Repeat and refine
Run repeated trials with the team. Record time, errors and review effort, then improve the steps that cause difficulty.
Review and hand over
Compare before and after, hand the guide and templates to the owner, and recommend continuing, adjusting or stopping.
What your team receives
One pilot workflow, working templates, a step-by-step guide with review points, a trial log, a before-and-after management summary and an owner handover plan.
Measure the actual work
Include review and rework in total task time, and track quality and tool costs. Separate released team capacity from actual cash savings. Deliverables are agreed in advance; no cost-reduction percentage or return is guaranteed.
Additional scope Production deployment, custom software, system or ERP integrations, licences and ongoing operations are excluded and require separate assessment and agreement.
Tell us the task, how often it occurs, your current tools and the person responsible, so we can assess the fit before quoting.
START HERE
CHOOSE THE FIRST DECISION
Choose a starting point before expanding AI investment
For needs beyond the 30-day package, choose the closest starting point and agree on outputs, participants, timing and completion criteria together.
Assess readiness and risk
Review use cases, data, access and risks to draft an AI policy and risk register your organisation can consider.
Try Digital Readiness →Design a measurable pilot
Choose one workflow and define its owner, baseline, KPI, guardrails and decision criteria in a pilot charter.
See an illustrative pilot →Plan how the team will use it
Define roles, learning, human review and measures in a 30–60–90 day adoption roadmap.
See possible outputs →Before agreeing scope and costs, we discuss the work with the problem owner, executives, operations and IT/Security as relevant. We confirm usable data, existing systems, user numbers, any licences or subscriptions, integrations and ongoing support needs. These costs depend on the chosen tools and agreed scope.
Share your AI challenge →01 / FOUNDATIONS
FROM EXPERIMENTS TO OPERATING MODEL
Build the foundations before scaling AI
AI Governance
Define an AI policy, risk tiers, approval flow, human review and the evidence needed before a use case goes live.
Data Readiness
Classify data and assess access, quality, retention, data boundaries and relevant PDPA requirements.
Adoption Design
Design workflows, roles, learning and measures so people use AI in real work—not only in a sandbox.
02 / AI STACK
CHOOSE BY CONTEXT, NOT HYPE
Choose tools that fit your data and existing systems
We are not tied to one vendor. We compare security, privacy, integration, user experience and total cost before a pilot begins.
Suitable for knowledge work, internal assistant and workflow with data boundary control.
Suitable for organizations that work on Microsoft 365 and want to start with the same data permissions.
Suitable for summarizing, analyzing, and writing documents that require long contexts and reviews.
Suitable for organizations that want to evaluate the integration with Google Workspace and existing data.
03 / ROADMAP
MEASURE → UNDERSTAND → DECIDE → ACT
Start small, with a path to scale
Assess
Explore goals, tasks, data, availability, and limitations.
Prioritise
Rank use case by value, feasibility and risk
Pilot
Experiment with real workflow with guardrail and owner
Scale
Measure adoption, productivity, quality, and value capture before expanding.
04 / DELIVERABLES
WHAT YOU TAKE BACK
What your team takes away
AI Policy Draft
Outline the principles of using AI, data scope, risk tier, and human review for the team to continue to apply.
Risk Register
List of risks, owners, control measures and evidence to monitor
Pilot Charter
pilot, owner, baseline, KPI, guardrail and decision-making challenges.
Adoption Roadmap
30-60-90 day plan for workflow, capability, change and value capture
05 / APPLIED EXAMPLE
FROM BUSINESS PROBLEM TO PILOT
Illustrative example: AI and automation in manufacturing
Scenario · Not a real customer result
Of the 24 ideas, 3 pilots are left for the first 90 days of testing.
Collect pain points from the work site, scoring impact, feasibility, data readiness and risk before selecting use case, not starting with the technology name.
Here's what you get: AI use case portfolio, scoring matrix, pilot charter, risk guardrail and roadmap 30-60-90 days
24 → 3Example of the use case screeningBaselineInitial numbers before experimentation90 daysHypothetical Pilot
06 / WORKING MODEL
WHO NEEDS TO BE IN THE ROOM
Align executives, IT and the people doing the work
Executive See the value, investment, risk and decision points that need to be unlocked.
IT/Security Team: See data boundary, access, integration, vendor and governance requirements.
Operations Team: Get workflows and tools that reduce real work with ways to measure results.
HR / L&D: Get an adoption plan that connects skills, roles, and behavior change.
07 / FAQ
QUESTIONS BEFORE THE FIRST PILOT
Questions to settle before the first pilot
Could company data reach an LLM provider?
Risks depend on the actual service and settings. Cloud AI may send data to a provider for processing. Consumer accounts and Business, Enterprise or API services may have different terms. “Not used for model training” does not mean that no data or logs are retained.
Before using real data, have IT and the data owner check the product and plan, data processing agreement (DPA), model-training use, retention and deletion, who can access the data, and what connectors or other providers receive. Then agree the permissions and boundaries your organisation accepts.
Start the pilot with synthetic or appropriately masked data, use only an approved environment, and check the current terms for that service before use. No account type guarantees the absence of all risk.
Examples of provider documentation to check: OpenAI — Business data · Anthropic — Data retention
Will AI change employees' jobs?
Our starting point is to reduce repetitive work and support better decisions—not to promise that roles will never change. We define workflows, owners, skills and measures so change is handled responsibly with employees.
What budget do we need to begin?
There is no need to buy a large platform first. Start with a bounded assessment or pilot, then evaluate total cost, realised value and the conditions for scaling based on your own evidence.
START WITH THE BUSINESS CHALLENGE
Discuss the work you want AI to help with
Use ThinkToFinish's central form to share tasks you want to improve, relevant information, and results you want to see.
Tell us your business challenge ↗Privacy policy · Information will only be used to understand the problem and get back to you.