Much of the enterprise conversation around AI still focuses on models, tools, and technical capability. Yet many organisations are discovering that the hardest part begins after the technology works.
The issue is no longer whether AI can automate a task, generate an output, or identify a pattern. The bigger question we see at Klarus is whether the organisation is ready to operate differently once AI is embedded in core processes.
This is particularly visible in finance. AI can support activities such as journal preparation, reconciliations, close management, reporting, and control execution. But the value does not come simply from replacing manual steps. It comes from changing the way decisions are made, reviewed, evidenced, and governed.
That is the “last mile” of AI adoption: the point where technical capability has to become part of the operating model.
For example, if AI proposes a journal entry, the process design must answer several practical questions.
- Who has the authority to approve or reject it?
- When is human review required?
- What evidence supports the recommendation?
- Who remains accountable once the entry is posted?
These are not technical questions. They are questions of decision rights, control design, and organisational accountability.
This is where we see many AI initiatives stall. A pilot may prove that the technology can work, but the business has not yet defined how people, systems, controls, and governance need to change around it. Without that redesign, AI remains an advisory tool rather than an operational capability.
At Klarus, we think scaling AI in finance requires three things to be designed together:
First, organisations need a clear decision architecture.
They must define where AI can act autonomously, where it can recommend, and where a human must remain in the loop. Ambiguity here creates either excessive caution or unacceptable risk.
Second, the control environment must evolve.
Controls designed for human-led processes do not always translate directly into AI-enabled workflows. Approval structures, monitoring routines, audit trails, exception handling, and testing procedures all need to be reconsidered.
Third, AI must be integrated into the reality of enterprise operations.
That means connecting it to ERP platforms, finance data structures, shared services processes, policies, and escalation routes. The detailed operational context matters. Chart-of-accounts structures, entity hierarchies, close dependencies, materiality thresholds, and control ownership all influence whether AI can operate reliably at scale.
For finance leaders, the opportunity is not simply to reduce effort. It is to improve decision speed, strengthen control, shorten cycle times, and give teams more capacity for analysis and business support. That opportunity will never be captured by technology deployment alone. It requires finance, technology, risk, audit, and business teams to work together on the operating model.
The organisations that succeed with AI will not necessarily be those with the most sophisticated models.
They will be those that redesign how work happens around those models. In finance, the last mile is where AI moves from pilot to performance. It is also where value is won or lost.
Reach out to Klarus if you want to know more about how we help our clients in finance close the value gap in the last mile.