Building a Visual Approach to Creating Microservices (Free Webinar)

To successfully scale agentic AI, enterprises need to approach it as a systems engineering problem, not just a model deployment exercise.

The network demands of today’s distributed AI workloads was apparent throughout this week’s annual Global NaaS Event (GNE).

Continued investment in the areas of APIs and automation will be the key for many companies to navigate 2023 and beyond.

Embracing an API-forward approach to digital transformation is one of the best ways to maintain business agility and scalability.

Digitizing Leadership Standard Work (LSW) is not the same as deploying a new tool and expecting behavior to change. Organizations that get real value from this approach are the ones that treat digital LSW as a management system, not a software rollout.

Building AI operations is a journey. The companies that succeed treat it as an ongoing operational capability, not a one-time technology project.

The next real shift with agentic AI will come from changing how work is done. Humans will move away from clicking through systems and toward commanding outcomes, with agentic layers coordinating execution across tools on behalf of the user.

The link between marketing and revenue is clearer than it used to be. That makes data issues harder to ignore, because the impact shows up directly in results. The data you need is already there. The question is whether it’s connected, consistent, and trusted enough to act on. That’s the work worth doing now.