Navigating an
AI-First World

Traditional consulting utilization is breaking

• Knowledge workers now produce the same output in a fraction of the time using AI.
• They are still billed as if working full-time — straining the hours-and-utilization model.
• The economics of "selling time" no longer reflect the value delivered.

Build AI-native delivery teams in each practice

• Small, dedicated groups inside every technology practice that learn by doing.
• Continuously experiment with AI-driven implementation and migration approaches.
• Large firms have spent ~18 months testing whether AI can replace or radically simplify enterprise implementations.

Open source as a go-to-market strategy

• Open-source accelerators, migration tools, and frameworks; publish code, demos, and prototypes.
• Let customers and partners use them freely.
• Monetize consulting, customization, adoption, migration, and support — not the software itself.

Code is becoming a commodity; adoption and change management stay hard and valuable.

The real value shifts from coding to adoption

• AI makes software development dramatically cheaper.
• Building software is no longer the hard problem.
• The hard problem is helping organizations adopt, integrate, and operationalize it.

Stakeholder management, training, process redesign, and change become worth more than coding.

Partner with AI vendors — especially Cursor

• Align closely with AI vendors such as Cursor, and potentially Anthropic.
• Create AI-driven migration offerings; show how systems can be rebuilt with AI tools.
• Become their implementation and adoption partner.
• Use vendor endorsement as a source of leads and credibility.

Replace expensive enterprise software with AI-generated alternatives

• Prove that many enterprise apps can be rebuilt at a fraction of the cost.
• Not just implementing platforms more efficiently — replacing them.

Shift internal focus from products to knowledge sharing

• Stop obsessing over building perfect products.
• Share successful AI experiments openly; publish code and lessons internally and externally.
• Create fast feedback loops where teams exchange working patterns.

Strategic concern: missing the AI platform wave

• Worry that opportunities with Anthropic and other major AI ecosystems may have been missed.
• Cursor seen as a more accessible partner with stronger near-term go-to-market.
• Attaching to a fast-growing AI platform could generate significant demand.