The evolution of 'agentic AI' extends beyond content generation to automating the connective tissue of business operations. Its future value is in initiating workflows that span departments, such as kickstarting creative briefs for marketing, creating product backlogs from feedback, and generating service tickets, streamlining operational handoffs.

Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.

Contrary to the view that useful AI agents are a decade away, Andrew Ng asserts that agentic workflows are already solving complex business problems. He cites examples from his portfolio in tariff compliance and legal document processing that would be impossible without current agentic AI systems.

Treat advanced AI systems not as software with binary outcomes, but as a new employee with a unique persona. They can offer diverse, non-obvious insights and a different "chain of thought," sometimes finding issues even human experts miss and providing complementary perspectives.

The true enterprise value of AI lies not in consuming third-party models, but in building internal capabilities to diffuse intelligence throughout the organization. This means creating proprietary "AI factories" rather than just using external tools and admiring others' success.

Instead of focusing on foundational models, software engineers should target the creation of AI "agents." These are automated workflows designed to handle specific, repetitive business chores within departments like customer support, sales, or HR. This is where companies see immediate value and are willing to invest.

Enterprises are finding immediate, high return on investment by using AI to port legacy codebases (like COBOL) to modern languages. This mundane task offers a 2x speed-up over traditional methods, unlocking significant infrastructure savings and even driving new developer hiring.

Borrowing from classic management theory, the most effective way to use AI agents is to fix problems at the earliest 'lowest value stage'. This means rigorously reviewing the agent's proposed plan *before* it writes any code, preventing costly rework later on.

Go beyond using AI for simple efficiency gains. Engage with advanced reasoning models as if they were expert business consultants. Ask them deep, strategic questions to fundamentally innovate and reimagine your business, not just incrementally optimize current operations.