New Data: Single-Player AI Is Stalling Work. Execs Want Shared AI
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As more and more businesses rely on AI, workers are fighting against an unexpected bottleneck: interface isolation. In a recent Salesforce survey of 250 U.S. executives, most AI use still happens one person at a time, and insights from each session tend to stay with that person.
The data makes the case:
Most executives (72%) already know what the fix looks like: instead of single-player AI that traps work in silos, they want multiplayer AI that works across the team with shared context, so value compounds instead of disappearing into a private chat window.
“The single-player era of AI made people faster,” said Rob Seaman, EVP and GM of Slack. “The multiplayer era is what makes a whole company smarter, together.”
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Even Within a Single Organization, There Is No Single AI Strategy
AI is jagged. Sometimes, AI agents can perform so reliably that a person’s main job is to delegate the tasks to AI and audit the results. Elsewhere, employees must capture missing context, curate AI’s output, or make and own the decision themselves.
Enterprise strategy should be variable too. Marc Escobosa, VP of Salesforce Futures, offers two questions to determine if an agent will excel or struggle with a task: does the agent have the data and context it needs, and is a company able to verify its answer? The answers to those two questions reveal four broad categories of work, each calling for a different role for humans.
Personal Agents Will Drive More Contact Center Traffic Than They Offset
Personal AI agents will happily sit through scratchy hold music. Then they’ll call back every few hours until your customer’s order ships, and they’ll call at 2 a.m. as easily as noon.
Kishan Chetan, EVP and GM of Agentforce and Service Cloud, argues that personal agents will drive more contact center traffic than ever, and that peak volume could become the new normal. But contact centers have handled seasonal spikes for years, and their teams already work alongside AI.
Getting to ROI: How Brands Turn Cost Centers into Revenue Engines with AI
Salesforce customers across industries recently described their agentic rollouts, sharing what worked, what surprised them, and what they’d tell others just getting started on their own AI journeys. A clear pattern emerged. Customers are seeing real results with AI by listening to their users, defining success, and building the guardrails to sustain it.
The pattern holds across industries, and Canon is one of several examples. One lesson Canon learned from its agentic deployment: delegating time-consuming tasks to agents gives humans the opportunity to create more value for the company. Canon is pivoting its service organization from a cost center to a value center, as agents field questions that don’t require a human touch.
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The AI Economy: Slack Steps Out From Behind the Desk
“Roughly 80 percent of the world’s workers don’t sit at a desk. They stock shelves, ship orders, and care for patients in their homes, and they’re often the only people a customer ever meets from a company. Yet much of the conversation about workplace collaboration and artificial intelligence has centered on the other 20 percent, the office workers whose days already run through laptops, chat apps, and now AI agents.
Slack wants to close that gap. On Monday, the Salesforce-owned company introduced Slack Frontline, a pared-down version of its app customized for shift workers in retail, warehouses, health care, and similar settings.”
VentureBeat: Salesforce Expands Agent Fabric With AI Evaluations to Trigger Shutdowns When Agents Fail
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Information Week: Claudeforce and What Comes Next for Agentic AI
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The 73-to-37 split stood out to us — enterprise executives reporting weekly stalls from isolated AI at nearly twice the rate of SMB executives.