Trinidad and Tobago is CARICOM's most industrialised economy, with GDP of roughly US$25 billion. Natural gas and LNG account for about 35–40% of GDP and over 70% of export revenue. That concentration pays the bills and is the country's largest risk, and governments have talked about diversification for three decades with little to show for it.
AI helps diversification in two ways. It can make the existing energy and petrochemical plants more reliable, which protects the revenue that pays for everything else. And it can make the non-energy sectors more competitive. This guide covers where the first AI project in each sector should go, and how to size the business case from your own numbers before a vendor sizes it for you.
The sectors and their first AI project
| Sector | First project | Who holds the data |
|---|---|---|
| LNG and petrochemicals | Predictive maintenance on high-value rotating equipment | Atlantic LNG, NGC, Point Lisas operators |
| Banking | Fraud detection and AML alert triage | Republic Bank, First Citizens, Scotiabank T&T |
| Point Lisas manufacturing | Process optimisation for yield and gas use | Ammonia, methanol and urea producers |
| Agriculture | Price and supply information for farmers and buyers | NAMDEVCO, Ministry of Agriculture, farmer groups |
| Tobago tourism | Visitor data analysis to target higher-spending travellers | Tobago House of Assembly, hotels, airlines |
LNG and petrochemicals: predictive maintenance
Atlantic LNG's four trains, with nameplate capacity of about 15 million tonnes a year, and the Point Lisas estate hold years of sensor readings, maintenance logs and incident reports. That is the raw material for models that spot equipment wear before a failure stops production.
Predictive maintenance models learn from vibration, temperature and pressure data alongside records of past failures. When they work, they give engineers warning of degradation early enough to schedule a repair during planned downtime instead of an emergency shutdown.
How to size it: take the number of unplanned outages on one class of equipment over the past three years, multiply by the average cost of each (lost production plus emergency repair), and assume the model prevents a share you are willing to defend to your finance director. If the answer is smaller than the cost of the data work, pick a different equipment class.
Where to start: Atlantic LNG and the National Gas Company already run SCADA and distributed control systems. The work is connecting that data to an analytics platform such as AspenTech, Honeywell Forge or a cloud equivalent, and writing the maintenance response procedure the alerts will trigger.
Banking: fraud detection and AML triage
Republic Bank, First Citizens and Scotiabank T&T operate across several Caribbean markets, process large transaction volumes and face growing anti-money-laundering requirements. Card and payment fraud is rising across the region.
Three uses are proven in comparable Latin American and Caribbean banks: real-time transaction scoring for fraud, AI triage of AML alerts so compliance staff spend time on the cases most likely to be real, and credit models that let banks lend to small businesses with thin credit files.
How to size it: for AML, count the alerts your team closed last year as false positives and the staff hours each took. For fraud, take last year's written-off fraud losses. Those two numbers, from your own ledger, are the ceiling on what a system can save.
Where to start: run a supervised pilot at one institution through the Central Bank's fintech regulatory hub, so the regulator sees the model's results before it goes live.
Point Lisas manufacturing: yield and gas use
Point Lisas produces ammonia, methanol, urea and steel for export into commodity markets with thin margins. Small gains in yield or gas use per tonne matter, and gas supply has been tight for years.
Process optimisation models use live sensor data to recommend, or directly adjust, production settings. Computer vision can inspect steel and packaged product for defects.
How to size it: multiply one year's output by the margin per tonne, then work out what a one percent yield gain is worth. Do the same for gas: annual gas consumption times the contract price times one percent. Those figures tell you what a pilot can reasonably cost.
Where to start: producers such as Yara and Methanex belong to global groups with their own digital and process-control programmes. Ask head office whether those tools can be applied to the Trinidad plants using local data. That is an internal request, not a new procurement.
Agriculture: price and supply information
Trinidad and Tobago imports about US$1 billion of food a year, and every government since independence has promised to cut that. Farmers are ageing, market information is poor and post-harvest losses on perishables are commonly put at 30–40%.
The useful AI here is modest: satellite and weather data to time planting, a price information service that connects farmers with buyers, and demand forecasts for wholesalers so less produce spoils. None of it fixes the age of the farming population, but it improves the economics for the farmers who remain.
Where to start: NAMDEVCO already publishes wholesale prices. A WhatsApp service that sends a farmer group weekly prices and a two-week weather outlook for their crops can be built cheaply on that data.
Tobago tourism: fewer, higher-spending visitors
Tobago's visitor numbers sit well below comparable islands, and it cannot win a price war for mass-market travellers. Its better market is eco-tourism, diving and wellness visitors who stay longer and spend more.
AI can analyse booking, airline and survey data to find which source markets and booking channels produce those visitors, then target marketing at them. On the island, hotels and guesthouses can use AI assistants to answer enquiries and put together itineraries in the evenings, when small operators have no staff on the desk.
Where to start: the Tobago House of Assembly and hoteliers should pool one year of anonymised booking data and ask which visitors stayed longest and spent most, before spending on new campaigns.
A 90-day starting plan
Month 1, energy data audit. List the equipment at one plant with the longest history of sensor data and logged failures. That is the first model's training set. If the logs do not say what failed, fix the logging first.
Month 2, banking sandbox. One bank takes an AML triage or fraud pilot to the Central Bank's regulatory hub, with a 90-day supervised test and an agreed success measure.
Month 3, manufacturing request. One Point Lisas producer asks its parent group for access to its process optimisation tools and names a local engineer to own the project.
The weak point in all of this is gas supply. If plants run below capacity for want of feedstock, maintenance and yield projects save less than the sizing suggests, and the case has to be rebuilt on current output, not nameplate.
What to do next
- Plant reliability managers: pull three years of unplanned outage records for one equipment class this month and cost them. That number decides whether a predictive maintenance pilot is worth doing.
- Bank compliance heads: count last year's AML alerts closed as false positives and the hours spent on them, then ask two vendors to price a 90-day triage pilot against that figure.
- Point Lisas plant managers: send a written request to your parent group's digital team for its process optimisation tools, naming one unit and one engineer.
- Farmer groups and NAMDEVCO: start a weekly price and weather message for one crop in one region, and track whether members sell at better prices after a season.
- Tobago House of Assembly: commission an analysis of one year of pooled booking data before the next marketing budget is set.
Frequently Asked Questions
Which regulator should a T&T bank talk to before piloting an AI fraud or credit model?
Start with the Central Bank of Trinidad and Tobago, which supervises commercial banks and runs a joint fintech regulatory hub with the Trinidad and Tobago Securities and Exchange Commission and the Financial Intelligence Unit. The hub is the right door for a question about whether a new AI-based process needs approval. For AML screening changes, the FIU's guidance on suspicious transaction reporting still applies, whatever software raises the alert.
Does T&T have a data protection law that affects AI projects?
The Data Protection Act 2011 was passed but has only been partly proclaimed, so most of its obligations on private companies are not yet in force. That does not remove contractual and sector duties, such as bank confidentiality. Treat customer data as if the full Act applied, because the government has repeatedly said it intends to bring it fully into force, and retrofitting consent and security controls later costs more.
What data does a plant need before a predictive maintenance model is worth building?
At least two to three years of time-stamped sensor readings for the equipment in question, plus maintenance records that say what failed and when. The records matter as much as the sensors: a model cannot learn a failure signature if work orders say only 'repaired pump'. If failures on a given machine are rare, start with condition monitoring and alert thresholds, and build the model once more failures are logged.
How much does a first AI pilot in T&T cost?
It depends mostly on whether the data is already clean and in one place. A fraud or document-processing pilot that uses a vendor's existing product on one bank's data is usually priced as a subscription plus integration work, while a custom plant model needs data engineering first. Ask vendors to price the pilot as a fixed fee with a written success measure, and to state what the full rollout would cost if the pilot meets it.
Where can T&T farmers get market and weather data for AI tools?
The Trinidad and Tobago Meteorological Service publishes forecasts and seasonal outlooks, and NAMDEVCO publishes wholesale market prices for produce. Those two sources are enough for a simple planting and pricing tool. Farmer groups can ask the Ministry of Agriculture's extension officers which crops have the most volatile prices, since those gain most from better timing.