Over the past year, we have been helping organizations explore how AI can be adopted within the enterprise. Interestingly, the discussions are often less about technology and more about business priorities. In many meetings, three themes consistently emerge: · AI is becoming a driver for innovation and funding. · AI adoption is viewed as a strategic requirement across the organization. · Intellectual property, governance, and data protection must remain non-

Many organizations today continue to rely on critical enterprise applications built on traditional technology stacks and tightly coupled middleware platforms. These systems were built to address the business requirements of their time. Many continue to operate reliably after more than a decade of enhancements, change requests, integrations, and operational support. The question many organizations face today is no longer whether these applications still work. The question is w

Over the past year, I’ve sat through countless conversations about “Agentic AI” — and now “Multi-Agentic AI.” These terms sound powerful, even futuristic. But when you look past the jargon, what you often find are approaches that are hard to operationalize: costs are difficult to forecast, outcomes can be hard to explain, and building trust in production settings becomes a real challenge. My perspective comes not from theory, but from hands-on experience building and testing