I've spent my career building the operating mechanisms that help organizations execute at scale—from aligning teams and developing capabilities to improving how decisions are made and results are measured.
Why enterprise AI investments struggle to translate into measurable business value—and the operating-model changes that may close that gap.
Across marketplace operations, commercial systems, and enterprise transformation, my work has centered on a recurring challenge: designing the decisions, incentives, capabilities, and operating mechanisms that turn strategic intent into sustained execution.
Operating-model design in practice: connecting decisions, resources, technology, and measurable performance.
Designed a local marketplace operating model that used geographic supply-and-demand signals, driver cohorts, and targeted incentives to anticipate shortages and deploy resources more deliberately.
The operating shift: From reactive driver incentives to anticipatory marketplace operations.
Redesigned how a financial advisory organization evaluated and deployed outbound sales capacity. Built an economic model connecting the cost of sales effort to customer conversion and expected lifetime value, then used those insights to develop targeted engagement programs, introduce CRM-integrated dialing automation, and inform executive investment decisions.
The operating shift: From volume-driven outreach to economically prioritized customer engagement.
Explore the case studyHistorical outcomes are contextualized within each case. The expanded program count reflects broader outreach coverage and calling capacity, not a like-for-like measure of individual productivity. Current-employer work is described at a high level elsewhere on this site.
Where AI investment meets financial accountability, organizational design, and the reality of getting work done.
A bank reports S$1 billion in AI and analytics value. The more revealing story is the management architecture behind the claim—and what the public evidence cannot independently prove.
Read the story ↗Why faster work and lower payroll aren’t interchangeable measures of AI value.
Read the perspective ↗Estimate the complete cost of an AI-enabled workflow, with a worked ledger that connects directly to the Value Realization Lab.
Read the cost guide ↗Why renewed human-service staffing didn’t mean abandoning AI—and what automation savings reveal about operating model decisions.
Read the case file ↗The UPS ORION case traces how route optimization became measurable operating benefit through changes to deployment, management, and field practice.
Read the UPS case file ↗Accessible analysis for curious readers, with separate research materials for those who want to inspect the reasoning.
Discover the operating choices behind AI outcomes without learning a methodology first.
Explore the DBS article ↗Inspect case studies, claims and methodological boundaries. The DBS technical dossier remains in review.
Enter the research library ↗Follow where AI-enabled task improvements become—or fail to become—measurable business outcomes. Explore the seven dimensions, the public-evidence standard, and the difference between observed results and verified economic attribution.
Interactive decision models that turn complex strategic questions into measurable scenarios.
Hours saved are an operational measure. Economic value depends on what the organization actually realizes.
Illustrative assumptions only. Released labor capacity is not automatically cash savings or incremental revenue. Validate attribution, costs, and outcomes before using results for investment decisions.
Model the full cost of an AI-enabled workflow, separate capacity from cash savings, and compare scenarios. Learn how to estimate the costs ↗
I'm Drew Peneton, a strategy and operations leader working at the intersection of enterprise technology, financial accountability, and organizational performance.
My career spans financial services, marketplace operations, and enterprise cloud—building systems that help organizations prioritize, execute, and measure business outcomes. Today, I'm exploring how those same disciplines can help enterprises realize sustainable value from AI.
My background includes a Master of Accounting and Financial Management and experience designing production AI tools, operating models, and executive performance mechanisms.