How Trust Unlocks the Full Potential of AI in the Workplace

What you need to know: 

• While executives are eager for AI-driven efficiency, worker concerns about privacy and security create an “AI adoption paradox.”

• At Slack, trust is embedded in AI development, ensuring security and human-centric design. This transforms the AI paradox into a scalable path for innovation.

 

Artificial intelligence has reached a watershed moment in business. What once lived in R&D labs is now reshaping how companies tackle their biggest challenges: finding talent, controlling costs and accelerating digital transformation. Agentic AI, in particular, promises faster workflows, fewer bottlenecks, and always-on responsiveness at a fraction of the operational cost.

A recent Slack survey confirms these priorities, revealing that 94% of executives plan to invest in AI over the next 12 months, and 84% are leaning towards agents to automate tasks, drive scale, and increase efficiency.

Yet the same data reveals that 41% of workers, not just executives, express concern about exposure risks, particularly privacy, copyright, and liability. This highlights what KPMG refers to as the AI adoption paradox: a tension between innovation and control, speed and certainty. 

Breaking the stalemate: operationalizing trusted AI 

Innovation moves at the speed of trust — and at Slack, that means embedding trust directly into how work gets done, not just in principle but in practice.

In the agentic era, operationalizing trust means building systems with transparency, security, and human-centeredness from the ground up. That looks like:

• Beginning with cross-functional oversight: Bring Legal, IT, Ops, and frontline teams together from the start to ensure comprehensive risk assessment.
• Enabling permissioning and observability by default: Employ platforms that automatically enforce access controls and maintain audit trails — no retrofitting required.
• Placing AI where work happens — not in disconnected silos: Embedding AI into existing workflows minimizes data movement and context switching, making it both faster and safer.
• Designing workflows with human guardrails: Build workflows with clear escalation paths, fail-safes, and checkpoints to keep humans in control of high-impact decisions.
• Measuring success in two dimensions — speed and safety: True innovation accelerates outcomes while enhancing security and compliance. Trust is a multiplier, not a constraint.

By selecting platforms with that in mind, organizations can transform the AI paradox into a scalable pathway for innovation.

Operationalizing trust: speed meets security with Slack

With trust at its core, Slack places these principles into practice.

From encryption and compliance, to admin control and responsible AI usage, Slack lays the foundation to adopt agentic AI with confidence. Customers retain full control over their data through standard workspace-level retention settings, customizable exports, and content deletion (Slack Trust Center).

Data is encrypted both in transit and at rest, and Slack can be configured to meet industry regulations and international security and data privacy standards. Compliance with several globally recognized regulations, security and data privacy frameworks, programs and standards enable customers to manage risk and secure data within their Slack environments.

AI and machine learning (ML) at Slack is no exception. Both are governed by the same commitment to trust:

• Customer Data is not used to train generative AI models.
• Data does not leak across workspaces. We do not build or train ML models in such a way that allows them to reproduce Customer Data.
• AI and ML at Slack only surfaces data the user is already permissioned to access.

Here’s how Slack turns principles into practice:

What trust looks like	How Slack enables it
Proactive risk prevention	Built in anomaly detection, DLP integrations and admin roles, real-time monitoring and alerts, and audit APIs. Customizable retention policies, access flows, and automated workflows to prevent oversharing. 2FA and SSO can be enabled for all users.
Security by default	AI in Slack never uses Customer Data to train generative AI models. Content stays within the OS. Message deletion, limited data export, and end-to-end encryption are enabled by default.
Minimal data exposure	Slack integrates privacy into every layer, from encryption to access controls to AI. It’s not an add-on; it’s foundational.
Granular user control	Granular admin settings for retention, deletion, export, and access — all configurable by workspace.
End-to-end security	Slack encrypts data at rest and at transit, provides real-time monitoring tools, and supports integrations with security platforms like CASBs and SIEMs. AI operates within these same guardrails.
Visibility and Transparency	AI in Slack shows what it generates and preserves full visibility into channels, threads, and activity to maintain oversight.
Human-in-the-loop oversight	Agents and AI always assist — never override — human decisions. AI in Slack can only see the same data that the requesting user can see.

More than checkboxes, they’re the architecture of scalable, trusted AI.

Trust is the foundation for innovation

In the next phase of work, trust is both table stakes and the greatest multiplier. While the AI paradox is real, it’s also solvable. With the right principles, AI doesn’t have to be risky, and its promise can be realized. Platforms built to preserve user trust resolve the tension between speed, innovation, and safety. Slack, by design, provides the secure and transparent foundation needed to bridge the gap between rapid AI advancement and the bedrock of user trust.

With innovationmoving this fast, the biggest risk isn’t adopting AI; it’s falling behind by standing still. Agentic AI introduces the ability to scale decision-making, boost productivity, and stay competitive in a landscape where speed, context, and trust are non-negotiable.

Crucially, systems that unify communication, data, and agentic AI within the flow of work are uniquely positioned to solve this paradox. They reduce friction, accelerate adoption, and embed governance where work actually happens — not as an afterthought, but as architecture.

Ultimately, innovation will belong to those who embed trust at every level of the stack. And when you use AI in Slack, your teams can move faster with the confidence that your data stays secure, your workflows stay compliant, and your trust stays intact.

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