Stop pouring capital through the cracks.
Companies throw money at AI, acquisitions, and other interventions designed to create value. The value realized is often lower than it could be, for predictable reasons.
Read insight →Decision qualityDecisionmaking stress test
How good is your company's decisionmaking process? Our rubric helps you evaluate and improve it.
Read insight →Modernization strategyMaking durable investments as technology changes
When adopting fast-changing technology such as AI, some aspects warrant deep investment while others should be staged or kept replaceable.
Read insight →Capital allocationProject sequencing matters for value realization
Evaluate operational improvement projects as a portfolio, and their value looks different from the sum of their individual business cases. Dependencies and sequence change what gets realized.
Read insight →DecisionmakingDecisionmaking under extreme uncertainty
It's hard to make good decisions in the fog of war. Enterprise environments are highly systemic, and many decisions have far-reaching consequences.
Read insight →Featured insights
What needs to be true for an AI initiative to work?
It's common to focus on AI tools and capabilities, but the dependencies and entanglements around them are at least as important to realizing value.
Read insight →AI adoptionWhy enterprise AI initiatives fail
A handful of predictable, avoidable mistakes prevent AI implementations from delivering their promised value.
Read insight →AI adoptionThe McKinsey ladder and the Talbot West onion
McKinsey found higher valuations associated with deeper AI integration. Talbot West had already described that progression with an onion metaphor, adding the quality of execution as another dimension.
Read insight →Revenue operationsAI in revenue operations
Talbot West's findings on AI in revenue operations, including how teams can replace parts of a sprawling SaaS stack with their own configured AI harnesses.
Read insight →AI engineeringThe importance of harness engineering
While headlines focus on AI models, harness engineering makes general-purpose AI effective for specialized tasks and domains.
Read insight →AI architectureComposable AI
A composable AI stack keeps your options open. You can replace individual components without rebuilding everything around them.
Read insight →More insights
Know your AI blast radius
Good AI governance accounts for how far an AI system's actions can reach and designs controls to match.
Read insight →AI architectureNeurosymbolic AI
Not everything called "neurosymbolic" actually is. And not every AI capability needs to be neurosymbolic.
Read insight →AI architectureCognitive Hive AI (CHAI)
Our modular, composable framework for deploying AI as a system of systems, drawing on principles of the Department of Defense's Modular Open Systems Approach (MOSA).
Read insight →AI Benchmark and Opportunity Assessment
Find out where you stand and what you can do about it.