• The Midas Report
  • Posts
  • Operational AI Is Driving the Next Big Edge in P&L Performance

Operational AI Is Driving the Next Big Edge in P&L Performance

4 min read

Author

Midas AI
August 18, 2025

Dells enterprise rollout shows how AI tied to core processes delivers revenue growth and cost cuts. Models that overlook this shift are already outdated.

AI as a Core Business Driver

If you still model companies as if AI is a side project, you are missing the story that actually reaches the income statement. The clearest signal is coming from operators, not slide decks. Dell Technologies put AI under executive ownership, set a two year deadline for transformation, and tied every initiative to profit levers. The result is measurable. For investors, the implication is simple, frameworks that ignore operational AI are betting against how value is now created.

Last year Dells CTO John Roese expanded into the role of Chief AI Officer and set a two year clock for the companys AI transformation. The mandate was blunt. AI projects must directly impact profit through revenue, margins, cost reduction, or risk mitigation. Dell prioritized four pillars where AI can change outcomes at scale, supply chain, sales, engineering, and customer service. As Roese put it, “We wanted to apply AI against the most impactful processes in the core differentiators of the business to improve our productivity.” This was not a lab experiment. It was an operating plan.

Designing for Scale

The operating plan started with process, not models. Dell reengineered workflows before applying AI so the company would not automate inefficiencies. That included end to end redesign in sales, along with substantial content cleanup. The company selected enterprise AI platforms and frameworks that could serve multiple use cases across departments, and architected the system for broad, secure, and scalable integration. It avoided isolated pilots and instead designed for scale from day one. In sales, AI powered tools were deployed to reduce preparation time, a concrete change that compounds across a large go to market organization.

The financial outcomes followed. In fiscal year 2025, Dell reported 8 percent revenue growth, 10 billion dollars in new revenue, and a 4 percent reduction in costs. The choices about where to apply AI line up with external evidence. A Stanford Artificial Intelligence Index Report cited supply chain, sales, engineering, and customer service as critical levers organizations can use to harness AI to save and generate revenue. Dells architecture and sequencing show why. When AI is attached to core differentiators, built to integrate across the enterprise, and measured against profit, it shows up in the P and L rather than in a demo.

Lessons for Investors and Operators

For founders and operators, the playbook is concrete. Define profit linked outcomes from the start. Redesign the process before adding the model. Choose platforms that can carry multiple use cases across the company, and build for security and scale. Put AI into the hands of teams where time is most valuable, like sales. Avoid one off pilots that cannot cross the chasm to production. The technical work matters, but the organizational design determines whether AI changes the cost structure and revenue engine.

For investors, the diligence checklist needs an update. Ask where AI is applied against supply chain, sales, engineering, and customer service. Look for process reengineering before deployment, not after. Check whether the architecture is built for secure, broad integration rather than a patchwork of pilots. Track whether management ties AI to revenue, margins, cost reduction, or risk mitigation and whether outcomes appear in reported figures. The companies doing this well are not just cutting a few points of expense. They are expanding the surface area where AI creates durable advantage across multiple functions.

The next edge in markets is the ability to recognize and model operational AI that hits the profit engine. Dells results are a proof point. Investors who cling to traditional analyst models that miss these drivers are not being conservative. They are betting against the future.