AI Theatre Is Over. Ops Wants Results.
2025 wasn't about flashy demos. It was the year operations teams separated the show from the work.
We sat through pilots that promised "cash visibility," "forecasting breakthroughs," and "one platform to connect it all." Then everyone went back to chasing yesterday's cash in spreadsheets. The lesson: demos impress, workflows move numbers.
What Actually Happened in 2025
The long-awaited AI wave didn't land the way slide decks predicted. Something better did: teams got clear on what modernisation takes inside real operations.
Progress came from boring, reliable execution. Less pitch. More proof.
Pilots Are Easy. AI-First Workflows Are Not.
Anyone can ship a pilot. Few can build an AI-first workflow that a payments team, banking ops, or treasury relies on every day.
Top-down mandates produced highlight reels. Real gains showed up when the people closest to the work designed, owned, and iterated their agents. Without the right data plumbing, controls, and ownership, AI stays stuck with the team that built it.
The Board-Level AI Gap
Only a third of UK startups and scaleups have AI expertise at board level, with larger scaleups far more likely to have it than smaller companies. That gap slows decisions, confuses priorities, and wastes budget.
If you're running ops, push for board literacy on AI-first workflows. It's the difference between chasing hype and funding what actually scales.
Breaking the 30% Automation Ceiling
Rules-based automation (if X, then Y) topped out around 30% of manual work. It collapsed on exceptions: merged payments, mistyped references, changing file formats. Humans bailed out the system.
AI flips the model. You set the outcome; the agent figures out the path. That's the leap from partial automation to near-total coverage. It's also how finance moves from time saved to materially better operations.
The Contained Value Breakthrough
The teams that won in 2025 didn't bolt AI on top of legacy workflows. They went use case by use case with tight scoping and auditability.
Automate reconciliation first. Then forecasting. Then cash visibility. Measure everything: reconciliation time down 75%, forecasting accuracy above 90%, audit prep from weeks to days. Retire manual processes as results land. Kill familiar workflows when the data says so, even if it stings.
Finance's Role Has Shifted
More CFOs now view treasurers as part of the C-suite. Digital tools are expected to dominate finance operations, with a meaningful share of tasks fully automatable.
Over the next five years, CFOs expect heavier emphasis on analytics, more scenario planning, and finance becoming a deeply embedded partner to the business. But you can't advise strategy while drowning in exception reports.
The Real Shift Is Cultural
This isn't a tech contest. It's a change in how teams choose to work.
For years, operations ran on institutional memory and heroic manual fixes. Modernisation starts when leaders stop accepting complexity, exceptions, and rework as "just how it is." The show ends when outcomes beat presentations.
What Operations Leaders Should Do Now
- Pick one contained use case. Define the outcome, owner, SLAs, data sources, and an audit trail. Ship it. Prove it. Then move to the next.
- Put doers in charge. Let the team that runs the process design and own the agent. Set guardrails, risk thresholds, and weekly metrics. Make changes fast.
- Fix the data plumbing. Standard IDs for customers/vendors, clean bank feeds, event logs, and clear system-of-record ownership. Bad inputs erase AI gains.
- Design for exceptions first. Map failure modes, human-in-the-loop steps, and escalation paths. No mystery workarounds. Everything observable.
- Stand up governance that enables speed. Model registry, change logs, access controls, and lower-environment testing. Compliance without clogging delivery.
- Publish an ops scorecard. Track hours of manual work retired, exception rate, time to clear exceptions, SLA hit rate, and audit findings. Tie wins to cost and cash impact.
- Retire dead workflows. If the data shows a better path, cut the old one. Don't run two processes to keep people comfortable.
- Upskill the team. Focus on agent design, prompt patterns, data literacy, and control frameworks. If you need a starting point, see AI Automation Certification or explore AI tools for finance.
Bottom Line
AI theatre entertained. Ops needs outcomes.
Start small, make it auditable, and retire manual work as you prove results. The gap between teams that do this and teams that keep presenting slides is getting wider by the week.
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