Moving beyond AI hype: building future-ready finance
AI promises a lot. Pressure is high. But the lesson from the last digital wave still stands: tech spend without smart execution stalls productivity. This year is about turning potential into performance - with clearer strategy, better workflows, and skills that compound.
The productivity opportunity (and the trap)
Finance teams see the opening. In CIMA's Future-Ready Finance: Technology, Productivity and Skills Survey Report, 59% of finance leaders prioritize new systems and software to improve productivity, and 48% focus on task automation. A strong 88% expect AI to reshape the profession within one to two years, while 59% rank data analytics and 54% cybersecurity as the next big movers.
That's the opportunity. The trap is assuming tools equal outcomes. New platforms without redesigned processes, skills, and clear metrics turn into expensive busywork.
Why productivity stalls isn't just tech
The main blockers are human and organizational, not technical. Respondents point to lack of skills and talent (50%) and the need for radical business change (39%). Beneath that are the daily drag factors: skills gaps (41%), low staff motivation (37%), poor coordination (32%), ineffective leadership (29%), unhealthy culture (28%), weak change management (26%), and inadequate communication (25%).
Translation: if people can't adapt, collaborate, and lead change, tools won't move the needle.
The skills imperative for finance
The sharpest gaps are where the work is heading: generative AI (46%), broader tech skills like big data, cloud, IoT, and robotics (37%), and data analytics (36%). The "soft" side still matters: communication, influencing, and critical thinking (33%) and business partnering (32%).
Hiring your way out won't scale. Leaders are doubling down on upskilling and on-the-job training - practical, context-driven learning embedded in work.
A practical playbook for CFOs and finance leaders
- Start with outcomes: Define 3-5 measurable targets (cycle time, forecast accuracy, time to close, cash conversion, controllable cost per transaction).
- Fix process before tech: Map the current workflow, cut handoffs, standardize inputs, then automate. Automate waste and you multiply waste.
- Sequence for quick wins: Target high-volume, rules-based tasks first (reconciliations, AP/AR matching, reports assembly). Prove value in 90 days.
- Invest in the skill stack: Data literacy for all, AI-assisted analysis for analysts, prompt and review skills for leaders, and change leadership for managers.
- Build AI guardrails: Access controls, data classification, human review for high-risk outputs, and clear model-use policies.
- Make it a product, not a project: Create a small cross-functional team that owns backlog, releases, adoption, and benefits tracking.
- Tie rewards to adoption: Recognize teams that use the new workflow, not those who heroically save the old one.
Where AI fits in finance right now
- Close and reporting: Draft variance narratives, flag anomalies, standardize commentary, and version control with approval steps.
- FP&A: Scenario generation, driver sensitivity checks, and dynamic commentary - with humans validating assumptions.
- Transactional finance: Invoice coding suggestions, supplier statement matching, exception triage.
- Controls and risk: Pattern spotting for unusual entries, access reviews, and policy checks to reduce manual sampling.
- Business partnering: Create first-draft insights and visuals from messy data so partners spend more time on decisions than assembly.
Metrics that keep you honest
- Days to close, forecast accuracy, and plan variance attribution time
- Cycle time per process (e.g., PO-to-pay, order-to-cash)
- Cost per transaction and touchless rate
- Employee adoption/utilization and time reallocated to analysis
- Control exceptions per 1,000 transactions and time to remediate
- Business partner satisfaction and decision lead time
90-day starter plan
- Weeks 1-2: Pick one process, baseline metrics, map the workflow, remove obvious steps, confirm data sources, write success criteria.
- Weeks 3-6: Pilot a minimal solution (automation + AI assist), document the new way of working, and set review checkpoints.
- Weeks 7-12: Expand to a second team, harden controls, train reviewers, and publish results against baseline.
Risk, cybersecurity, and controls
Cybersecurity ranks high for finance leaders (54%), and for good reason. Treat AI like any other model: define intended use, monitor drift, log prompts/outputs for key processes, and require human sign-off where financial exposure exists. For a structured approach, the NIST AI Risk Management Framework is a useful reference.
NIST AI Risk Management Framework
Upskill your team without slowing delivery
- Embed micro-learning into the weekly routine tied to real work (15-30 minutes, then apply immediately).
- Train "peer coaches" inside finance to review prompts, data logic, and controls.
- Standardize prompts, templates, and definitions to reduce rework and improve auditability.
Tools and training to move faster
If you want a curated starting point for finance-specific AI tools and training paths, this resource shortens the search and helps you align tools to skill levels and roles.
AI tools for finance (curated list)
Bottom line
Technology adoption doesn't equal productivity. The finance function that wins pairs focused outcomes with redesigned workflows, targeted upskilling, and tight governance. Do that, and your AI spend turns into shorter cycles, better decisions, stronger controls - and a real edge.
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