Use real public data, machine learning, and GenAI to run smarter, faster public services.
Why This Workshop Matters
Governments and large public institutions are sitting on massive data assets, but most decisions are still made with static reports, manual triage, and gut feeling.
International work from the OECD shows that governments are increasingly using AI to design better policies, make better decisions, and improve service quality, but struggle with skills, data quality, and governance. At the same time, OECD’s Data-Driven Public Sector framework is clear: treating data as a strategic asset is now a core capability of modern government, not a “nice to have”.
In the Gulf and globally, authorities like SDAIA are positioning data and AI as central to national visions and smart-city strategies, tying AI directly to dozens of strategic goals and driving large-scale adoption in public services and infrastructure.
On the ground, cities are already running AI-powered waste systems, traffic and incident monitoring, and command centres (e.g. Dubai’s “Dubai Live” real-time AI command hub; AI-based waste and civic issue monitoring pilots in Indian cities).
But most public-sector teams still ask the same questions:
- “Where do we start?”
- “Which use cases actually work with our data?”
- “How do we keep this ethical, explainable, and auditable?”
This workshop is designed to answer those questions in a concrete, operational way.
Call us to learn more about this data-driven public systems with AI & ML – operational workshop
Overview
Title
Data-Driven Public Systems with AI & ML – Operational Workshop
Duration
2 days, full-day, in-person
Format
- Instructor-led, interactive
- Short frameworks → real public-sector examples → group design work → live demos
- Joint audience: operations, policy, and technical staff in the same room
Level
- Accessible for non-technical managers and policy makers
- Valuable for data, BI, and IT teams who want clearer ML patterns and realistic pilots
- No requirement to code during the workshop (all demos are instructor-led)
Purpose
To help public institutions and large organisations design practical, low-risk, high-impact AI & ML use cases on their own data and services, with a clear understanding of:
- What operational AI can do today
- Which ML patterns fit public-service problems
How to make systems transparent, auditable, and defensible
Who This Workshop Is For
Ideal for teams in:
- Ministries and central agencies (finance, health, labour, education, transport, social services, environment, interior, justice)
- Cities and municipalities (smart city teams, operations, urban planning, utilities)
- Regulators, audit institutions, anti-fraud and compliance units
- Large public enterprises and NGOs running citizen-facing programmes
Typical participants:
- Directors and heads of department
- Programme, policy, and operations managers
- Data / BI / analytics teams
- Digital transformation, PMO, and innovation units
You do not need a deep technical background. The content is designed so leaders, analysts, and engineers can work together and leave with a shared language and plan.
What Participants Will Be Able To Do
By the end of the 2 days, participants will be able to:
- Identify high-impact AI & ML opportunities for their own services:
- Risk scoring for inspections, audits, and fraud detection
- Demand forecasting for clinics, schools, transport, or social programmes
- Anomaly detection in payments, logs, or sensor data
- Smart-city style operational use (waste, traffic, civic incident detection)
- Risk scoring for inspections, audits, and fraud detection
- Understand the data → model → decision pipeline for public services, and where ML models plug in (vs where a dashboard or rule is enough).
- Sketch system blueprints: data sources, ML pattern, decision points, human oversight, and feedback loops.
- Recognise governance and ethics requirements in a public context: fairness, explainability, appeal, algorithm registers, and logs for audits.
Leave with a 90-day operational AI plan: one or two pilots, data steps, governance steps, and owners.
Signature Operational AI Patterns Covered
Throughout the workshop we teach repeatable patterns your teams can re-use:
- Risk Scoring & Prioritisation
- Example: rank inspections, audits, or cases by predicted risk or impact.
- Example: rank inspections, audits, or cases by predicted risk or impact.
- Demand Forecasting & Capacity Planning
- Example: forecast clinic visits, school enrolment, or traffic flows to plan resources.
- Example: forecast clinic visits, school enrolment, or traffic flows to plan resources.
- Anomaly Detection & Early Warning
- Example: spot unusual payment patterns, sensor readings, or incidents early.
- Example: spot unusual payment patterns, sensor readings, or incidents early.
- Routing, Triage & Assignment
- Example: route complaints, tickets, or field tasks to the right teams automatically.
- Example: route complaints, tickets, or field tasks to the right teams automatically.
- GenAI for Operational Text
- Example: summarise case notes, cluster complaints, or extract structured fields from free text using LLMs with guardrails.
- Example: summarise case notes, cluster complaints, or extract structured fields from free text using LLMs with guardrails.
These are illustrated using public-sector and smart-city examples, not just generic e-commerce or marketing stories.
2-Day Agenda
Day 1 – From Public Data to Operational AI Use Cases
Session 1 – Data-Driven Public Systems: The New Operating Model
Objective: Build a shared understanding of what “data-driven” and “AI-enabled” actually mean for public services.
Topics:
- How governments are already using AI and ML to design better policies, improve services, and strengthen relationships with citizens
- Examples from smart cities and national strategies (e.g. AI command centres, AI-optimised waste and traffic management, SDAIA & Vision 2030).
- The data-driven public sector model: data as an asset for policy, operations, and innovation.
Exercise – Service Map
Each team maps one core service (e.g. inspections, benefits, permits, maintenance) and notes:
- What data is collected today
- Where decisions are made
- Pain points: backlog, delays, blind spots, complaints
Session 2 – Operational AI Pattern Library for Public Services
Objective: Show concrete, repeatable ways AI & ML improve day-to-day operations.
We walk through the five patterns above with sector-specific examples, such as:
- Fraud detection and public finance anomaly detection
- Smart waste and incident management in cities (AI cameras, smart bins, route optimisation).
- Predictive analytics in policing, health, and planning.
Exercise – Use Case Scoring
Teams choose 3–4 candidate use cases and score them on:
- Impact on citizens / budget / risk
- Data availability
- Implementation complexity
- Political / reputational risk
They then select 1–2 use cases to carry forward through the rest of the workshop.
Session 3 – Inside the Data → Model → Decision Pipeline
Objective: Demystify what actually happens in an AI/ML system.
Topics (explained in accessible language):
- Data layer – admin data, sensors, logs, open data, complaints, text.
- Preparation & features – cleaning, joining, creating useful signals.
- Model layer –
- Risk models, forecasting models, anomaly detection
- Where GenAI fits (summaries, extraction, clustering of text)
- Risk models, forecasting models, anomaly detection
- Decision layer – dashboards, workflows, score-based prioritisation, triggers.
- Feedback & monitoring – performance over time, data drift, human feedback.
Live Walkthrough (demo)
You show a small example: from spreadsheet → cleaned dataset → simple model → ranked list or dashboard. No code needed from participants, but they see all steps.
Exercise – Pipeline Sketch
For their chosen use case, teams draw a pipeline blueprint:
- Inputs (specific data sources)
- Model or analytics pattern to use
- Who sees the outputs and how
- Where humans approve, override, or review decisions
Session 4 – Data Readiness & Capability Check
Objective: Keep ideas ambitious but grounded.
Topics:
- Data readiness levels (from “spreadsheets in silos” to “well-governed analytics platforms”).
- Skill sets: policy owners, data stewards, analysts, ML engineers, IT operations.
- Interoperability and data sharing as enablers for GovTech.
Exercise – Maturity Heatmap
Teams rate their current maturity on:
- Data (quality, access, interoperability)
- People (skills and capacity)
- Tools (BI, ML, data platforms)
- Governance (policies, approvals, oversight)
They tag each proposed use case as:
- “Can pilot in 6–12 months”
- “Needs data/coordination first”
- “Longer-term ambition”
Day 2 – Designing Pilots, Guardrails & 90-Day Roadmaps
Session 5 – Designing High-Impact, Low-Risk AI Pilots
Objective: Turn ideas into pilots that can realistically be executed.
Topics:
- Choosing operational pilots that are: valuable, observable, and reversible.
- Choosing between:
- Dashboards and analytics
- Classic ML models
- GenAI components (e.g. summarisation, retrieval)
- Dashboards and analytics
- Defining success: service-level metrics, accuracy/coverage trade-offs, staff and citizen experience.
Exercise – Pilot Design Canvas
Teams design one flagship pilot, detailing:
- Problem statement and target users
- Data required and data owners
- Model / pattern to use
- Where the output appears in the workflow
- Clear success metrics (3–5 indicators)
Session 6 – Governance, Ethics & Public Accountability
Objective: Ensure AI deployment strengthens trust instead of undermining it.
Topics:
- Key principles from OECD and leading AI governance initiatives for the public sector: beneficial use, fairness, transparency, accountability, robustness.
- Concrete risks: biased data, hidden automation, lack of appeal, vendor black-box models.
- Practical guardrails:
- Keeping humans in the loop
- Algorithm registers and documentation
- Logging, explanations, and audit trails
- Impact assessments and proportionality
- Keeping humans in the loop
Exercise – Risk & Guardrail Canvas
For their pilot, teams identify:
- Affected groups and possible harms
- Where bias could enter (data, model, usage)
- Minimum transparency needed (internally / publicly)
- Concrete guardrails they commit to (e.g. manual review thresholds, clear appeal process, public documentation)
Session 7 – Optional Deep Dive: Technical Flow (for mixed audiences)
If the group includes technical staff, we add a light technical deep dive:
- How a simple risk model / forecaster would be trained and evaluated on historical data.
- How a GenAI/RAG component might be plugged in for text-heavy tasks (e.g. complaints summarisation, policy search).
- How logs and monitoring are set up in practice.
Non-technical participants focus on implications, not code.
Session 8 – 90-Day Roadmaps & Commitments
Objective: Leave with owned, time-bound plans.
Each team produces a 90-day roadmap that includes:
- Pilot plan – milestones, owner, internal partners, and decision gates.
- Data work – the one or two data-quality or integration steps that unlock the most value.
- Governance step – e.g. draft internal AI guidelines, propose an AI register, or launch a small cross-unit AI working group.
- Capability step – follow-up training, hiring, or a partnership with a university/vendor.
Teams share their roadmaps for feedback and alignment.
Call us to learn more about this data-driven public systems with AI & ML – operational workshop
Example Use Cases You Can Mention on the Website
You can list examples like:
- Risk-based inspections for food safety, environment, tax or labour.
- Smart waste collection and incident monitoring using AI and IoT.
- Demand forecasting for health centres, schools, or transport routes.
- AI-assisted command hubs for city operations and emergency response
- GenAI assistants that help staff search policy, summarise cases, and triage citizen requests.
You can add a line that actual scenarios and data will be adapted to the country, city, or institution.
Institutional Outcomes
After this workshop, your organisation will:
- Share a common, non-hype understanding of what AI & ML can do for public operations.
- Have 1–2 carefully designed pilots that are realistic in scope, governed, and tied to measurable outcomes.
- Know what data, skills, and governance steps are needed to move from spreadsheets to AI-assisted decisions.
- Be able to brief internal or external technical teams with clear, well-structured requirements instead of vague “AI ideas”.
FAQs
1. Is this a technical training?
Answer: No. It’s operational and strategic. Technical people will see how things fit together, but leaders and policy teams will fully follow without coding.
2. Do we need to bring our own data?
Answer: Not required, but recommended. We can work with anonymised or representative data. Even without data in the room, we can design pipelines and pilots based on your current systems.
3. Is this focused on one country?
Answer: The frameworks are global (OECD, World Bank, smart city cases), but examples and discussions can be tailored to your national strategy and local context, including Vision-style agendas and national AI authorities.
4. What is the difference between this and a generic “AI in Government” seminar?
Answer: Generic seminars stay at concept and policy level. This workshop is hands-on and pattern-based: you leave with concrete use cases, system blueprints, governance steps, and a 90-day plan your teams can execute.
Ready to Take the Next Step?
- Schedule a call with our trainer
Haris Aamir
Trainer
Lincoln School