Creating operating leverage with AI
Generative AI changed the production economics available to a marketing services company. Leadership needed to determine where the technology could create operating leverage, what investment that opportunity justified, and how to deploy it without sacrificing quality.
Talbot West identified leverage points across the organization, modeled returns, compared implementation paths, redesigned workflows, established controls, trained the workforce, and deployed AI-enabled production systems across functions.
- 01Map leverage
- 02Build investment case
- 03Design operating architecture
- 04Prepare information and systems
- 05Deploy and tune
- 06Govern and enable
- 07Increase capacity
Map the leverage
We decomposed production across functions: where employees spent time, which activities constrained throughput, what information each activity required, where judgment materially affected quality, and which activities repeated often enough to justify automation.
That separated high-return applications from technically feasible uses with weak economics.
Some activities supported direct automation. Others supported AI augmentation with employee control. High-context activities retained human ownership. Review remained explicit where errors could propagate into client deliverables.
Build the investment case
We modeled expected productivity improvement against implementation cost, capacity constraints, and likely commercial impact.
The analysis showed which deployments could support greater revenue with the existing team, which constraints would move next, and where additional investment had the strongest expected return.
Leadership used that analysis to prioritize deployment and align capital and organizational attention around the highest-leverage opportunities.
Design the operating architecture
We rebuilt production flows around the selected interventions.
Inputs and context
Specify the information each workflow required.
Human and machine roles
Assign execution, judgment, review, and exception handling.
Automation boundaries
Constrain repeatable generation and transformation where outputs could be evaluated.
Quality controls
Place approval and review where errors could reach client deliverables.
Employees retained responsibility for client context, judgment, exception handling, quality review, and decisions that could not be reduced reliably to automation.
Several workflows crossed functional boundaries and required coordination across production, account management, leadership, and supporting systems.
Prepare the information and systems
We assessed the data, source material, business context, and system access required by each workflow.
Where existing systems provided reliable inputs, we used them. Where repeatable execution required more consistent context or tighter orchestration, we added the necessary structure and automation.
Simple augmentation stayed simple. Higher-volume and cross-functional workflows received more engineering where repeatability, quality, or scale justified it.
Deploy and tune in production
We introduced the selected workflows into live production and measured them against operating requirements.
Production exposed where AI outputs were reliable, where additional context improved performance, where human review remained necessary, and where workflow design created friction.
We adjusted prompts, information inputs, automation steps, review points, and human responsibilities as those patterns became visible.
Successful patterns expanded.
Weak patterns were revised or removed.Govern and enable
Talbot West defined operating controls alongside workforce training.
Governance
Appropriate-use boundaries, human review requirements, quality expectations, responsibilities, and escalation paths.
Training
Tool use, context construction, output evaluation, quality control, and workflow-specific practices.
Adoption
Employees progressed from guided use to independent execution as proficiency increased.
Low-risk, repeatable tasks could operate with greater automation. Client-sensitive or judgment-intensive outputs retained stronger human oversight.
Increase capacity without increasing headcount
The redesigned production system allowed the existing organization to support substantially more commercial volume without adding labor at the previous rate.