AI productivity in banking
Banks can capture real productivity gains with AI, but only when workflows and controls are redesigned together. The path is audit-ready workflow change, clear model risk boundaries, and metrics that prove throughput and quality.
The question behind this piece
Most banks are testing AI, but many efforts stall at pilots or trigger control anxiety. Leaders want measurable productivity, but they cannot trade away privacy, model risk discipline, or customer harm controls. How can banks deliver real productivity gains in specific workflows while staying audit-ready and scalable?
Why this matters now
AI capability has advanced quickly in drafting, summarization, classification, and workflow assistance. That makes productivity improvements feasible in functions historically constrained by writing, review, and coordination.
At the same time, scrutiny has increased. Internal audit, model risk, privacy, and conduct teams expect clear boundaries, evidence, and monitoring for any AI-enabled workflow, especially when customer outcomes could be affected.
The productivity imperative is also sharper. Margin pressure, compliance workload, and customer expectations for speed leave less room for manual rework loops and inconsistent execution.
AI does not create productivity. Workflow redesign does.
Our perspective
Treat AI productivity as workflow transformation with controls embedded by design. The winning approach is simple: pick a workflow, redesign the work, embed AI with guardrails, and measure outcomes weekly until the gains hold.
Start with workflows that are high-volume, document-heavy, and already somewhat standardized. Good candidates include case summarization in service, KYC refresh packages, adverse media triage, credit memo drafting support, disputes intake classification, policy interpretation support for frontline teams, and internal knowledge retrieval for operations.
Before touching tools, define the baseline and the target. If you cannot measure it, you cannot scale it. Keep the scorecard tight: cycle time, backlog, rework rate, error rate, escalation and exception rates, cost per case, and a small set of quality outcomes that matter to risk and compliance.
Then redesign the workflow around a human-in-control pattern. AI should handle first drafts, first summaries, or first triage. Humans approve, override, and remain accountable for decisions. Make the boundaries explicit: where AI is allowed, where it is prohibited, what data can be used, and what evidence must be captured.
Controls must live inside the workflow, not outside it. A practical control-by-design pattern includes technical enforcement of approved use cases, logging of prompts and outputs for investigation, grounding requirements for customer-impacting content, clear escalation paths when confidence is low, and monitoring for drift, error patterns, bias signals, and customer harm indicators. Bring model risk and compliance into design, not as late-stage reviewers, and tier the controls based on workflow risk.
The winning banks treat AI as a controlled production capability, not an innovation experiment.
Strathen Group works as an extension of your operations, risk, and analytics leaders to turn key priority workflows into a repeatable, bank-grade pattern you can scale across functions.
What you can expect from the engagement:
- Workflow shortlist and selection logic (volume, standardization, risk tier, and ROI drivers).
- Workflow redesign artifacts (future-state steps, roles, decision points, and exception paths).
- Control-by-design blueprint (allowed uses, prohibited data, logging, grounding, overrides, escalation).
- Evidence trail and audit pack (what is recorded, where it lives, and how it is reviewed).
- Measurement and cadence (weekly scorecard, owner routines, fix backlog, and decision log).
- Adoption enablement (scenario-based training, “good use” definitions, and coaching prompts).
We can also provide light ongoing support after the setup to help teams scale to additional workflows, sustain performance, and keep controls healthy as usage grows.
