Teams | Collaboration | Customer Service | Project Management

Enterprise AI is moving fast, but results aren’t keeping pace with investment. A 2026 commissioned study conducted by Forrester Consulting on behalf of Simpplr uncovered a foundational problem that better models alone won’t fix. Fragmented data, lack of governance, and disconnected systems are limiting what AI can do, and addressing them starts with rethinking the foundation of the digital workplace.

Call centers have never been pressured. Customers expect quicker responses, and agents feel more burned out. Similarly, U.S. companies still lose an estimated $75 billion annually from poor service. That’s why most businesses can’t help but adopt AI and automation in call centers. The global call center AI market is projected to reach $101.77 billion by 2034 at a CAGR of 17.76%. And 78% of organizations are already using AI in at least one business function.

Amazon Web Services has launched two AI agents to investigate production incidents and run penetration tests. The company has “aggressively” priced these agents, according to Forbes senior contributor Janakiram MSV, to challenge the staffing economics of traditional DevOps and security., There are big implications. Teams will now be evaluating whether routine operations should remain manual at all.

If you’re managing products at an enterprise scale, you know that roadmapping isn’t just about picking features. It’s about managing chaos. Between shifting stakeholder demands, engineering bottlenecks, and the nightmare of cross-team dependencies, you need a platform that does more than just look good in a slide deck. Generic project tools simply don’t have the “Product DNA” required for this level of play.

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.