Finance leaders push AI workflow redesign over headcount cuts
Finance leaders see AI as critical to their organisations, but most doubt they're ready to use it effectively. A CIMA discussion this week highlighted a stark gap: 88% of 1,500 senior finance leaders call AI a game-changer, while only 8% feel well prepared to adopt high-impact technologies.
That readiness gap matters more than job losses, panellists said. The real risk is poor implementation - buying tools without changing how teams work.
Process redesign comes before tools
Organisations often install new software while keeping old workflows intact, said Bart van Ark of the Productivity Institute. Staff end up with fresh technology but stale processes.
Finance functions offer a test case for whether AI can deliver measurable gains. The technology can strip out repetitive work, sharpen forecasting, and back better decisions. None of that happens without deliberate workflow redesign and clear rules about where humans stay in control.
Alexander Ilkhan, a treasury practitioner and consultant, warned against framing automation chiefly as a way to cut headcount. That messaging kills staff engagement and slows adoption.
Teams respond better when AI is presented as a tool to kill routine tasks and free up time for judgment-based work. In finance, that means less manual processing and more focus on analysis, planning, and business partnering.
Leadership and data discipline matter more than the tools
Readiness depends less on access to the latest software than on leadership, training, and governance. Skills gaps and weak motivation pose bigger barriers than the technology itself.
Many employees are already using AI tools at work without formal approval. That makes it urgent for leaders to set clear rules on data use, acceptable applications, and review processes - while still allowing teams to test and learn.
Fred Fowler of Coty said data quality is the starting point for any serious AI project in finance. Inconsistent data can block progress long before organisations reach advanced automation.
Data management is an ongoing discipline, not a one-off cleanup. Maintaining master data, standards, and common definitions is essential if AI systems are to reliably support planning, reporting, and analysis.
CFOs shape adoption success
Chief financial officers and senior finance executives sit at a critical junction. Their teams bridge operational data, internal controls, and management decisions. They can either remove barriers to AI adoption or entrench them.
Successful adoption requires continuous learning, not one-off pilots. Organisations need leadership backing, internal champions, tool access, and baseline training so staff understand how systems work and can check their outputs.
Large language models work well for language-heavy tasks like drafting process documents. They should not be trusted for numeric accuracy without controls.
Measure progress beyond labour savings
Progress should be tracked across several dimensions: time saved and efficiency gains; fewer errors and smoother processes; and broader strategic benefits that may take longer to quantify.
Public and not-for-profit organisations stand to gain from AI-driven productivity improvements because of the volume of routine cognitive work they handle. The same implementation challenges apply.
The panel's core argument: start with the outcomes you want to improve, set controls around data and usage, and train staff to work with the technology rather than fear it. Data quality is step one.
For finance leaders looking to build capability in this area, an AI learning path for CFOs can help translate strategy into practice. Understanding AI agents and automation is also essential for designing workflows that actually work.
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