In ERP projects, AI is changing the economics of data migration. Instead of weeks of manual mapping, finance teams can classify legacy fields, flag anomalies and surface reconciliation breaks before go-live. You need to GetAgentIQ! Learn more at getagentiq.io
A lot of AI commentary still treats this as a software cycle.
Build a model. Launch a feature. Add a copilot. Repeat.
But the numbers coming out of the market suggest something bigger is happening.
When companies are talking about AI infrastructure spend on a scale of tens or even hundreds of billions, that is not a normal product refresh.
That is a replatforming event.
And replatforming events reshape entire industries.
The obvious story is model performance.
The less obvious story is what happens downstream.
If AI reduces the cost of design, code generation, analysis and support work at scale, then a lot of software that looked defensible starts to look fragile.
Not because the products are bad, but because the production cost of “good enough” is collapsing.
That is why the most important question for operators is no longer “Which model is best?”
It is “Where does durable value still live when capability gets cheaper every quarter?”
A few answers seem increasingly clear:
- proprietary workflow data
- customer trust
- system integration
- operational reliability
- distribution
In other words, the moat is moving.
This is also why agents matter.
Once models are embedded into workflows, value shifts from standalone software features to systems that can actually execute work safely across the business.
That is a much bigger design challenge than chat.
And a much bigger opportunity.
The next winners in AI may not be the loudest model labs or the flashiest app demos.
They may be the teams quietly building the infrastructure, workflows and governance layers that make agentic systems usable in the real world.
That is where the market is heading.
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Most finance forecasts fail because they update too slowly. AI can turn FP&A into a rolling decision engine, spotting variance early, modelling scenarios fast, and giving leaders time to act before month end. You need to GetAgentIQ! Learn more at getagentiq.io
Finance AI gets interesting when FP&A moves from static budgets to rolling scenario models. Faster reforecasts, earlier variance signals, and better planning conversations give finance teams more time to influence decisions.\n\nYou need to GetAgentIQ!\n\nLearn more at getagentiq.io
Most ERP implementation conversations still focus on process maps, data migration and training plans.
All important, of course.
But AI is changing the standard finance leaders should now expect from an ERP programme.
The question is no longer just: will the new system process transactions cleanly?
It is: will it help finance spot risk earlier, explain variance faster and support better decisions across the business?
Gartner said in March 2025 that generative AI, machine learning and cloud ERP are among the top technologies expected to receive future investment in finance. That tells you something important. Finance teams are not thinking about AI as a side experiment anymore. They are starting to see it as part of the operating model.
From my perspective, after 20+ years in finance systems and transformation, the best ERP projects are now designed with three layers in mind:
1. Strong transactional control
2. Clean, trusted data
3. Intelligence on top
If you skip the first two, AI will only amplify bad process and messy data.
But if you get the foundations right, AI can genuinely add value, for example by identifying posting anomalies, surfacing supplier or customer trends, improving forecast assumptions, and reducing the time finance spends hunting for answers.
That is where I think many organisations need to reset the conversation. AI should not be bolted on at the end of an ERP project as a shiny extra. It should be considered early, with a clear view of business outcomes, governance and data quality.
In finance transformation, the winners will be the teams that combine solid ERP discipline with practical AI use cases.
You need to GetAgentIQ!
Find out how we can help you navigate your AI adoption journey at getagentiq.io
Most ERP implementation conversations still focus on process maps, data migration and training plans.
All important, of course.
But AI is changing the standard finance leaders should now expect from an ERP programme.
The question is no longer just: will the new system process transactions cleanly?
It is: will it help finance spot risk earlier, explain variance faster and support better decisions across the business?
Gartner said in March 2025 that generative AI, machine learning and cloud ERP are among the top technologies expected to receive future investment in finance. That tells you something important. Finance teams are not thinking about AI as a side experiment anymore. They are starting to see it as part of the operating model.
From my perspective, after 20+ years in finance systems and transformation, the best ERP projects are now designed with three layers in mind:
1. Strong transactional control
2. Clean, trusted data
3. Intelligence on top
If you skip the first two, AI will only amplify bad process and messy data.
But if you get the foundations right, AI can genuinely add value, for example by identifying posting anomalies, surfacing supplier or customer trends, improving forecast assumptions, and reducing the time finance spends hunting for answers.
That is where I think many organisations need to reset the conversation. AI should not be bolted on at the end of an ERP project as a shiny extra. It should be considered early, with a clear view of business outcomes, governance and data quality.
In finance transformation, the winners will be the teams that combine solid ERP discipline with practical AI use cases.
You need to GetAgentIQ!
Find out how we can help you navigate your AI adoption journey at getagentiq.io