For established software companies with sluggish growth, the path forward is clear: find a way to become relevant in the age of AI. While they may not become the next Harvey, attaching to AI spend can boost growth from 15% to 25%, the difference between a viable public company and a sale to a private equity firm.

For incumbent software companies, surviving the AI era requires more than superficial changes. They must aggressively reimagine their core product with AI—not just add chatbots—and overhaul back-end operations to match the efficiency of AI-native firms. It's a fundamental "adapt or die" moment.

An effective AI strategy requires a bifurcated plan. Product leaders must create one roadmap for leveraging AI internally to improve tools and efficiency, and a separate one for external, customer-facing products that drive growth. This dual-track approach is a new strategic imperative.

In the age of AI, 10-15 year old SaaS companies face an existential crisis. To stay relevant, they must be willing to make radical changes to culture and product, even if it threatens existing revenue. The alternative is becoming a legacy player as nimbler startups capture the market.

The threat to established SaaS companies is not just technological but also psychological. Simply adding AI features to an existing product like Photoshop may not be enough if AI creates entirely new workflows. Survival depends on 'human agency'—bold leadership willing to cannibalize existing products and fundamentally reimagine their business for an AI-centric world.

The current market leaves no room for mediocrity. SaaS companies are either at the forefront of AI, delivering jaw-dropping value and capturing new budget, or they are being displaced. Hiding behind long-term contracts is a temporary solution, as there is no longer a middle ground.

Incumbent software vendors face a crisis: customers aren't churning, but all new enterprise budget is directed at AI. This traps legacy platforms as stagnant 'systems of record' while AI applications built on top capture all future growth.

Established software leaders should not try to innovate on all new AI technologies organically. A more effective strategy is to let the VC community fund early-stage bets, then use strong balance sheets to acquire the proven winners and integrate them into existing platforms, as Salesforce has done.

To transition to AI, leaders must ruthlessly dismantle parts of their existing, money-making codebase that are not competitively differentiating or slow down AI development. This requires overcoming the team's justifiable pride and emotional attachment to legacy systems they built.