AI advantage will not come from simply using the latest model. It will come from the data, workflows, governance, infrastructure and operating capabilities built around AI that are harder to replicate.
Read more →AI is making cybersecurity teams faster, but it is also expanding the attack surface faster than many organizations can govern it.
Read more →Companies can drive meaningful business impact by scaling incremental, manageable AI use cases instead of adopting a “big bang” approach. This means focusing on building capabilities, managing risk, and climbing a practical AI maturity curve.
Read more →For the majority of specialized, repetitive agentic tasks, SLMs are set to take the lead — offering sufficient capability, lower latency, and dramatically reduced costs.
Read more →Agentic AI initiatives often underwhelm because teams fixate on the agent technology itself, rather than the end-to-end business workflow it needs to improve. Success depends on redesigning the entire process, rigorously evaluating agent output to prevent "AI slop," and recognizing that complex AI agents are not always the right tool for the job.
Read more →By using a maturity model that assesses not just your tech stack, but your organization's capabilities across vision, people, delivery, and measurement.
Read more →