Close your
AI Impact Gap
AI budgets are increasing, but measurable revenue, cost and cycle-time returns are not. We help you get the most from your AI investments.
TRUSTED BY MID-MARKET LEADERS ACROSS INDUSTRIES
56%
of 4,454 CEOs across 95 countries report neither higher revenues nor lower costs from AI.
26%
of all enterprise AI spend is wasted across models, tools, and infrastructure (Harness FinOps 2026).
95%
of organizations reportedly report zero return despite $30–40B in enterprise GenAI investment.
REASONS FOR THE AI IMPACT GAP
Why do 95% of organizations report zero return on their AI investments? We identify six critical roadblocks preventing teams from realizing measurable business values.
Wrong business priorities
Projects are chosen based on organizational excitement or tech hype rather than strategic, high-value business goals.
Low organizational readiness
Teams lack the operational structure, hands-on training, and workflow alignment required to deploy AI solutions daily.
Inadequate infrastructure
Legacy technology stacks fall short of the high-speed data pipelines and elastic computing needed to power AI models.
Poor data quality & availability
Siloed, unstructured, or dirty data limits accuracy, leading to untrustworthy model behaviors and failed workflows.
Weak compliance & governance
Lack of clean standards around data privacy, ethical boundaries, and security oversight introduces operational risks.
Escalating compute & API costs
Unoptimized token usage, hardware licensing, and compute costs scale rapidly, wiping out project ROI margins.
HOW STRATEGY OF THINGS AI (SoT AI) HELPS
Three services to get the most impact from your AI portfolio investments.
Assess
AI Maturity Assessment
Understand your organization’s readiness for AI across nine dimensions with the SoT AI Enterprise Reference Model.
Manage & Govern
SoT AI Portfolio Compass
Assess, plan, prioritize, manage, and govern an AI portfolio that creates measurable business impact.
Benchmark
AI Market Intelligence
Bring the latest developments, trends, and best practices to your AI initiatives.
Latest Insights from Strategy of Things
Practical governance models, portfolio prioritization methodologies, and CAIO strategies for mid-market leaders.
Considerations for Picking the Right AI Use Cases for Your AI Portfolio
From boardrooms and the C-suite to the shop floor, everyone is looking for opportunities to integrate AI into their operations. However, identifying the "right" AI use cases to work on is not a trivial task. Deciding which of the "right" use cases gets the funding is often a process fraught with limited data, subjectivity, organizational politics and inconsistency. Key considerations are described in this article.
AI Is Everywhere. Mid-Market Impact Is Not. You May Be Following the Wrong Playbook.
Most mid-market companies are doing AI. Pilots are running, vendors are engaged, and budgets have been committed. But when you step back and look at the business as a whole, the impact is not there. The problem is not the technology. It is the playbook. This post explains why the enterprise AI playbook does not work at mid-market scale, what a different approach looks like across three critical zones, and where to start.
AI Project Prioritization: Building an AI Portfolio That Delivers
Building an AI portfolio that delivers enterprise impact requires a prioritization methodology that is significantly more rigorous than most organizations expect. This article explains what effective AI project prioritization actually requires, how to move from a ranked list to a deliberately constructed portfolio, and what separates organizations that treat prioritization as a discipline from those that treat it as a meeting.
Your AI Projects Are Competing Against Each Other. You Just Can't See It.
AI is everywhere. Enterprise impact isn't. The gap between the two is not a technology problem, it is a structural one. Most organizations manage AI as a collection of one-off projects, missing the synergies, wasting resources, and blocking their own highest-value initiatives. This post makes the case for AI project prioritization and portfolio thinking as the operating discipline that changes that.
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