SOT Blog & Insights
Frameworks, industry research, and CAIO strategies for modern intelligence
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.
How the Right AI Model Translates Into Decisions, Strategy, and Results.
Knowing where your organization stands across nine layers of AI capability is the beginning, not the end. Part 2 of our enterprise AI framework series moves from diagnosis to action: how the SoT AI Operating Model drives build-buy-partner decisions, use case prioritization, market research, and AI strategy, and the questions every operational and technology leader should be asking right now.
The Secret to AI Success Isn’t the Right Tools. It’s the Right Model.
Eighty-eight percent of organizations use AI. Only 31 percent have scaled it enterprise-wide. The gap is not the technology. It is the structure surrounding it. The SoT AI Enterprise Reference Model is a nine-layer enterprise AI framework that maps every capability an organization needs to build and sustain AI at scale, from infrastructure through strategy, technical and organizational dimensions treated as one inseparable system.
Are Your AI Investments Building a Competitive Advantage or Just Cutting Costs?
AI tools are not an AI strategy. Most mid-market companies are investing in AI that makes their existing operations faster or cheaper. While this is real value, it rarely is the kind that changes a competitive position. This post discusses how the companies that are pulling ahead are thinking about it differently.
The AI Scaling Problem Nobody Talks About Enough
AI pilots are succeeding. Enterprise impact is not following. Across industries, organizations are accumulating pilot wins while the structural and infrastructure gaps that prevent those wins from reaching business performance go unaddressed. This article draws on direct observations from fractional Chief AI Officer engagements to identify the scaling problems most organizations are not talking about enough, and what leadership teams can do to close the gap.
Future-proofing your AI Infrastructure
AI infrastructure investments are moving faster than most organizations can manage. Platforms are fragmenting. Vendors are pivoting. Regulatory requirements are tightening. For mid-market companies, a wrong bet is not just expensive. It is a setback there is no easy budget to recover from. This post introduces a practical framework for future-proofing your AI infrastructure: a continuous lifecycle management strategy that helps CEOs, COOs, and CAIOs protect their investments, maximize flexibility, and manage change deliberately.