According to McKinsey, 74% of enterprise AI initiatives fail to deliver expected value. That is not a technology problem — it is an execution problem. After working with dozens of enterprises across manufacturing, energy, banking, and hospitality, DGT has identified a consistent pattern: the gap between AI ambition and AI value is almost always organizational, not technical.
The Four Execution Gaps We See Repeatedly
First, there is the data readiness gap. Enterprises invest in sophisticated ML models but feed them fragmented, ungoverned data from disconnected systems. Second, there is the governance gap — no clear ownership of AI outcomes, no framework for responsible deployment, no way to measure what 'success' actually means.
Third, there is the integration gap. AI models that live in notebooks or sandboxes never reach the business processes where they could create value. And fourth, there is the change management gap — the people who need to use AI outputs do not trust them, do not understand them, or were never consulted in the design.
The enterprises that succeed with AI are not the ones with the best models. They are the ones with the best execution discipline.
DGT's Approach: Assess, Align, Activate, Scale
Our AI Execution Model addresses all four gaps systematically. We start with a rigorous assessment across 12 enterprise dimensions — not just technical readiness, but organizational readiness, data maturity, governance structures, and change capacity. Then we align AI investments to specific business outcomes with measurable gates.
The activation phase is where most consultancies stop and most enterprises stall. DGT stays through production deployment, integration with existing platforms (Salesforce, SAP, ServiceNow), and the critical first 90 days of adoption. Finally, we build the internal capability to scale — because the goal is not perpetual consulting dependency, it is self-sustaining AI operations.
- Assess: 12-dimension enterprise AI readiness evaluation
- Align: Business-outcome mapping with investment prioritization
- Activate: Production deployment with platform integration
- Scale: Internal capability building and governance frameworks
What This Means for Your 2026 AI Strategy
If your AI initiatives are stalling, the answer is probably not more technology. It is better execution architecture. Start by asking: Do we have a clear owner for AI outcomes? Is our data actually ready for the models we want to build? Are the people who will use AI outputs involved in the design? And do we have a governance framework that enables speed without sacrificing responsibility?
If you cannot answer yes to all four, you have an execution gap — and that is exactly what DGT helps enterprises close.