Most leaders don't need more ideas.They need better judgment under pressure.
We work with senior leaders and revenue executives applying AI where accountability is real and consequences scale. Our focus is upstream of tools and downstream of results. Before implementation. Before rollout. Before automation.
Most failures are decided here, long before anything is built.
The issue is not adoption.It is decision quality upstream.
"We're not seeing much impact from AI."
"Adoption has been slower than expected."
"It's helpful, but not really changing anything."
"It's hard to measure ROI."
"We thought we'd be further along by now."
This is not a tool problem.
Teams buy Copilot, ChatGPT, Claude, or similar tools. Leadership expects people to start using them. Someone gets tasked with figuring out AI on top of their day job. A few people use it, most don't, and no one can point to business impact. So the tools get layered on top of existing work, and nothing really changes.
- 01No clear decision on where AI should and should not be used.
- 02No ownership for outcomes.
- 03No workflows actually redesigned.
- 04No definition of what success looks like.
Most failures are not technical. They are misaligned trade-offs that quietly erode margin, distort incentives, and compound operational risk.
The work centers on four questions.
Not prompts. Not tools. The decisions that determine whether any of it creates advantage or risk.
Clarity before implementation.Judgment before automation.
Align on where AI creates leverage, and where it creates risk.
A focused session with leadership to frame the real decision. Where AI belongs, where it doesn't, and what is actually at stake before anyone commits capital, people, or reputation.
The Decision Context Diagnostic. Structured clarity before implementation begins.
We map where AI should and should not be applied, prioritize use cases by value and feasibility, sequence what happens in what order, and identify what should not be built at all. You engage implementation partners with the decisions already made.
Advisory and senior-team working sessions that hold the standard.
Ongoing work that reduces friction, aligns judgment across the leadership team, and increases decision velocity without lowering standards. Clear framing reduces organizational drag. That is leverage leaders control.
// Engagements are scoped in conversation, not sold from a menu.
DecisionLabAI.The pre-implementation decision layer.
The operating system for better business decisions.
DecisionLabAI is the productized layer beneath our advisory. It sits upstream of implementation teams and defines the decision layer that determines what they are asked to build. Most AI tools answer prompts. DecisionLabAI structures judgment.
AI Opportunity Mapping
An enterprise-wide inventory of where AI could apply, before anyone commits to a pilot.
Prioritization Framework
Every use case scored on business value against real feasibility.
Sequencing Plan
An ordered roadmap so investment compounds instead of scattering across tools.
Do Not Build Analysis
The low-ROI and redundant initiatives worth killing early, named explicitly.
Executive Decision Alignment
Clear ownership and governance, so decisions have a home before vendors arrive.
Built for accountable people.Not for AI tourism.
✓ This is for
- Senior leaders and revenue executives operating under real accountability.
- Founders and investors pressure-testing high-stakes decisions before they commit.
- Institutional decision-makers shaping where AI capability gets built.
- Operators who value judgment over hype.
- Teams making decisions where failure is visible.
× This is not for
- Teams looking for AI tips, tools, or training.
- Anyone shopping for prompt tricks or productivity hacks.
- Buyers who want a tool deployed and a box checked.
- Work where the stakes, and the standards, are low.
Glen Caruso
- Enterprise dataOracle Data Cloud
- Enterprise platformAdobe Experience Cloud
- CategoryFinServ & capital markets
- InstitutionalUSC
- Teaching AI since2023
I come from the operator side of the table. Enterprise revenue teams. Complex organizations. Public companies. Environments where decisions affect revenue, reputation, and people at scale.
In most organizations, AI is layered onto existing workflows without redesigning the decision structures beneath them. That creates speed without clarity, automation without accountability, and leverage without alignment. My focus is upstream of tools and downstream of results.
I translate emerging AI capability into practical advantage without hype, noise, or compliance theater. Engagements range from executive advisory to senior-team working sessions designed to reduce friction, align judgment, and increase decision velocity without lowering standards.