AI is beginning to change the economics of CTRM implementation in the same way it is reshaping IT consulting more broadly. As AI-enabled tools make consultants more productive, the traditional model of billing more people for more hours comes under pressure, and CTRM buyers are starting to ask why an implementation needs so many consultants at all. The integrators that adapt, moving from selling hours and headcount to selling expertise, speed and outcomes, are the ones likely to thrive.
For decades, implementing a CTRM system has followed a familiar model.
A commodity trading company selects its software. A system integrator is brought in. Teams of consultants spend months understanding the business, documenting processes, configuring interfaces, mapping data, writing specifications and building integrations with the wider technology landscape.
The larger the transformation, the larger the implementation team.
But what happens when technology starts removing much of the work that has traditionally justified those consulting hours?
That question is becoming increasingly relevant across the wider technology consulting industry. Artificial intelligence is allowing companies to analyse code, understand data, generate documentation, accelerate testing and automate development at a fraction of the time previously required.
The same forces are coming to CTRM. And the implications for system integrators could be significant.
Why was the CTRM implementation model built around complexity?
CTRM implementations are complicated for good reasons.
Commodity businesses have complex trading strategies, pricing structures, logistics, inventory positions, contracts, exposures and accounting requirements. They also have years and sometimes decades of legacy systems surrounding the CTRM. A new CTRM therefore rarely operates in isolation.
It needs to exchange information with ERP systems, exchanges, market data providers, banks, warehouses, terminals, brokers, risk platforms, data warehouses and reporting tools.
Historically, much of the work involved in connecting all these pieces has required people. Lots of people.
Consultants analyse existing systems. Developers build interfaces. Business analysts document requirements. Project managers coordinate workstreams. Testers validate results. Teams then repeat much of the process during UAT and deployment.
The commercial model has consequently been relatively straightforward:
More complexity → more people → more time → higher implementation cost
AI challenges that equation.
How is AI changing the economics of CTRM implementation?
Consider some of the work involved in a typical technology transformation.
- Understanding legacy code
- Identifying dependencies
- Creating technical documentation
- Mapping data
- Writing interface specifications
- Generating test cases
- Analysing errors
- Creating reports
- Writing code
These are precisely the kinds of activities where AI is increasingly capable of accelerating human workers. The wider IT industry is already beginning to discover that a task which previously required several consultants for several weeks may eventually require a much smaller team working with AI-enabled tools. CTRM implementations will not be immune to this trend.
In fact, some areas of CTRM implementation may be particularly susceptible because much of the work involves structured data, repeatable processes, well-defined interfaces and established commodity trading workflows.
For a CTRM buyer, the question is likely to become:
“Why does this implementation require so many consultants?”
That is a very different question from the one buyers traditionally asked. Historically, a large implementation team could be reassuring. It suggested that the vendor and integrator were committing significant resources to the project.
But clients are increasingly looking at the relationship between resources and outcomes, rather than simply the number of people assigned to a project.
If AI allows a team of five experienced people to achieve what previously required fifteen, the client is unlikely to regard the additional ten people as automatically adding value. The “day-rate” model could come under pressure.
This potentially creates a particularly uncomfortable question for system integrators. Many implementation engagements have traditionally been based, directly or indirectly, on time and materials. More consultants working more hours generally means a larger project fee. But AI introduces a productivity paradox.
If consultants become significantly more productive, the client should benefit from that productivity. A consultant who can complete in two days what previously took ten days is delivering a better outcome. But if the commercial model is built around billing ten days, there is an obvious tension.
This is already prompting wider technology buyers to consider alternative commercial structures, including:
- Fixed-price implementations
- Milestone-based payments
- Smaller implementation teams
- Greater use of client resources
- Greater responsibility being retained in-house
CTRM buyers are likely to ask similar questions.
How is the system integrator's traditional role changing?
There is another important development. Modern CTRM platforms increasingly provide more functionality out of the box. Cloud deployment has removed much of the infrastructure burden. APIs have simplified connectivity. Standardised integration patterns have reduced the need for bespoke interfaces. Low-code and configuration tools can reduce development effort. AI is now accelerating activities across analysis, development and testing.
This means that the traditional role of a system integrator, building a large amount of bespoke technology around a CTRM platform, may become less important.
The future integrator may instead need to provide something much more valuable; a deep commodity expertise, recent implementation experience and the ability to solve genuinely complex business problems. That is a very different proposition from providing large numbers of people.
Will CTRM expertise matter more or less as AI takes on routine implementation work?
There is an important distinction here. AI can make a consultant more productive. It does not automatically make them a commodity trader. Understanding why a trader needs a particular pricing mechanism is different from knowing how to configure one. Understanding the commercial implications of a change to a contract is different from generating a specification. Understanding the relationship between a physical cargo, its logistics, pricing events, inventory and financial exposure requires domain knowledge.
This could make specialist CTRM expertise more valuable, even as the overall number of consultants required falls. The winning implementation team may therefore not be the largest. It may be the team with the deepest understanding of the client's business and the CTRM platform, equipped with technology that allows them to deliver significantly faster.
“Do we need an integrator at all?”
This is perhaps the most uncomfortable question for the industry.
Historically, many companies needed external consultants because they lacked the expertise or resources to implement complex enterprise software themselves. But as software becomes easier to configure and AI makes internal teams more productive, that balance can change.
A trading company with experienced business users, a capable IT team and a modern CTRM platform may increasingly be able to undertake more of the implementation itself. Instead of outsourcing the entire transformation, it might bring in specialists only for the areas where external expertise genuinely adds value.
What role do CTRM vendors have to play in this shift?
This doesn't mean CTRM vendors can simply sit back and watch the market change. Quite the opposite. Vendors need to make their platforms easier to implement. That means configuration rather than custom development, standardised integrations rather than bespoke interfaces, accessible APIs, better documentation, automated testing and tools that allow experienced clients to take greater ownership.
The best CTRM platforms should make implementation progressively less dependent on armies of external consultants. That is not just good for clients. It can also make implementations faster, more predictable and less risky.
Does a smaller implementation team mean a smaller implementation?
This distinction is important. Reducing the number of consultants involved in a project should not mean reducing the quality of the implementation. The objective should be to eliminate low-value activity and concentrate resources on the decisions that actually matter. A highly experienced implementation team, supported by automation and AI, can potentially deliver more than a much larger team operating traditional processes.
There is also a broader trend that could accelerate this change. The technology landscape itself is becoming less dependent on bespoke integration. Modern SaaS applications increasingly expose APIs as standard. Data platforms provide common connectivity frameworks. Cloud infrastructure reduces the need for traditional middleware. AI can assist with data mapping and interface development.
At the same time, CTRM platforms are becoming more capable. If the underlying systems are easier to connect and configure, there is simply less integration work to sell.
That creates a difficult structural challenge for integrators whose economics have historically depended on the complexity surrounding enterprise software.
What happens next?
The CTRM implementation industry is unlikely to disappear. Complex commodity businesses will continue to need specialist expertise. Large transformations will still require programme management. Data migration will still require careful planning. Integration will still matter. Testing, controls and regulatory requirements will not disappear.
But the shape of the implementation industry is likely to change.
- Fewer people may be required
- Those people may need different skills
- Projects may become shorter
- Clients may take more responsibility themselves
- Commercial models may move away from billing predominantly for hours towards charging for outcomes
What's the lesson for CTRM buyers?
For companies evaluating their next CTRM, this creates an opportunity.
Don't just ask a vendor or integrator:
“How many consultants will you put on the project?”
Ask:
- How much of the implementation is configuration versus bespoke development?
- How much integration is genuinely required?
- Can the CTRM vendor provide all personnel for the implementation?
- What can our internal team own?
- Which activities can be automated?
- How long should implementation realistically take?
- What determines the implementation cost?
- Are we paying for people, or for outcomes?
- How much knowledge will remain with us after go-live?
The answers could tell you far more about the true cost and risk of a CTRM implementation than the size of the proposed project team.
What will the future CTRM implementation team look like?
The next generation of CTRM implementations may look very different from those of the past.
Instead of a large army of consultants surrounding a complex technology project, imagine a smaller group of highly experienced commodity and technology specialists, supported by automation and AI, working alongside the client's own team.
Fewer hand-offs. Less documentation for documentation's sake. Less bespoke development. Less integration complexity. Faster testing. Shorter implementation cycles. And, ultimately, a greater focus on getting the trading business operational.
For CTRM system integrators, that may sound like a threat. But it could also be an opportunity.
The integrators that thrive will be those that move away from selling hours and headcount and towards selling expertise, speed and outcomes.
See how does Amphora measure up? for how Amphora itself answers the question this piece raises, having implemented its CTRM for over 30 years without using a third-party system integrator.
Because if AI is about to make the traditional consulting model obsolete, the answer isn't to resist the change. It is to become much more valuable than the work AI can replace.
