Teams | Collaboration | Customer Service | Project Management

When Elena Verna joined Lovable, an AI-native startup, she wasn’t chasing another growth role. “I actually wanted to retire before I hit Lovable,” she admits. After leading growth at Dropbox, SurveyMonkey, Miro, and Amplitude, she’d seen the same patterns play out again and again. “I couldn’t do another gig where I felt like I’m doing growth 101. Onboarding flows, lifecycle emails, A/B tests.

Agentic AI autonomously plans, reasons, and acts to resolve complex service tasks. Learn how it works, its key features, benefits, and real-world use cases.

Fragmented stacks don't just frustrate customers. They quietly raise costs, slow agents, and cap what AI can actually do.

The Zendesk Resolution Platform is more than just speed – it ensures AI delivers accurate service resolutions.

When Aubrey Rogers, Head of Creative Operations & Production at Asana, kicks off a new creative campaign, she doesn’t start with execution. First, she needs to understand what’s happening and why. This often means a team kickoff, go-to-market decks, and streams of back-and-forth conversations happening on multiple platforms. In theory, Rogers should have everything she needs after kickoff. She’s defined the strategy and aligned stakeholders.

HR teams spend a huge chunk of their day-to-day time on mediocre and repetitive tasks. The irony is that this also forces employees to lose productivity hours while searching for the right answer to their routine queries. This aligns perfectly with what Deloitte’s Global Human Capital Trends 2025 survey reported. According to it, 41% of HR teams’ time is spent on work that doesn’t contribute to the value their organizations create.

HR shared services were designed to centralize support and improve efficiency, but many teams today are still overwhelmed with repetitive employee requests, from leave queries and payroll questions to policy clarifications and onboarding support. Despite having shared service models in place, HR teams continue to face delays, rising ticket volumes, and increasing pressure to meet SLA expectations. The core issue isn’t centralization, it’s the reliance on manual case handling.

How Time Doctor uses AI models to turn activity data into actionable benchmarks.