Businesses currently present disconnected personalities to customers across sales, service, and marketing. AI agents can bridge these silos to create a seamless, long-running dialogue that remembers context throughout the entire customer journey, fundamentally transforming the customer relationship.

The new generation of AI automates workflows, acting as "teammates" for employees. This creates entirely new, greenfield markets focused on productivity gains for every individual, representing a TAM potentially 10x larger than the previous SaaS era, which focused on replacing existing systems of record.

Beneath the surface, sales 'opportunities,' support 'tickets,' and dev 'issues' are all just forms of work management. The core insight is that a single, canonical knowledge graph representing 'work,' 'identity,' and 'parts' can unify these departmental silos, which first-generation SaaS never did.

AI agents can manage the entire buyer lifecycle from first touch to upsell. This removes human capacity constraints, allowing companies to merge siloed go-to-market teams into a single, cohesive unit focused on the customer journey.

Stop thinking of sales, marketing, and support as separate functions with separate tools. AI agents are blurring these lines. A support interaction becomes a lead gen opportunity, and a marketing email can be sent by a 'sales' tool. Prepare for a unified go-to-market operational model.

The current moment is ripe for building new horizontal software giants due to three converging paradigm shifts: a move to outcome-based pricing, AI completing end-to-end tasks as the new unit of value, and a shift from structured schemas to dynamic, unstructured data models.

Intercom's CEO predicts that companies will abandon separate AI agents for sales, service, and onboarding. A single, coordinated "customer agent" is necessary to avoid conflicting goals and create a seamless, high-touch experience for every user.

Companies struggle to get value from AI because their data is fragmented across different systems (ERP, CRM, finance) with poor integrity. The primary challenge isn't the AI models themselves, but integrating these disparate data sets into a unified platform that agents can act upon.

Traditional software required deep vertical focus because building unique UIs for each use case was complex. AI agents solve this. Since the interface is primarily a prompt box, a company can serve a broad horizontal market from the beginning without the massive overhead of building distinct, vertical-specific product experiences.